Let's read some awesome transfer learning / domain adaptation papers.
Here, we list some papers by topic. For list by date, please refer to papers by date.
这里收录了迁移学习各个研究领域的最新文章。
- Awesome Transfer Learning Papers
- Survey
- Large models
- Theory
- Per-training/Finetuning
- Knowledge distillation
- Traditional domain adaptation
- Deep domain adaptation
- Domain generalization
- Source-free domain adaptation
- Multi-source domain adaptation
- Heterogeneous transfer learning
- Online transfer learning
- Zero-shot / few-shot learning
- Multi-task learning
- Transfer reinforcement learning
- Transfer metric learning
- Federated transfer learning
- Lifelong transfer learning
- Safe transfer learning
- Transfer learning applications
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A Survey of Heterogeneous Transfer Learning [arxiv]
- A recent survey of heterogeneous transfer learning 一篇最近的关于异构迁移学习的综述
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Review of Large Vision Models and Visual Prompt Engineering [arxiv]
- A survey of large vision model and prompt tuning 一个关于大视觉模型的prompt tuning的综述
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IEEE TNNLS-22 Towards Personalized Federated Learning
- A survey on personalized federated learning 一个关于个性化联邦学习的综述
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2022 Transfer Learning for Future Wireless Networks: A Comprehensive Survey
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2022 A Review of Deep Transfer Learning and Recent Advancements
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2021 Domain generalization: IJCAI-21 Generalizing to Unseen Domains: A Survey on Domain Generalization | 知乎文章 | 微信公众号
- First survey on domain generalization
- 第一篇对Domain generalization (领域泛化)的综述
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2021 Vision-based activity recognition: A Survey of Vision-Based Transfer Learning in Human Activity Recognition
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2021 ICSAI A State-of-the-Art Survey of Transfer Learning in Structural Health Monitoring
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2020 Transfer learning: survey and classification, Advances in Intelligent Systems and Computing.
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2020 迁移学习最新survey,来自中科院计算所庄福振团队,发表在Proceedings of the IEEE: A Comprehensive Survey on Transfer Learning
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2020 负迁移的综述:Overcoming Negative Transfer: A Survey
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2020 知识蒸馏的综述: Knowledge Distillation: A Survey
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用transfer learning进行sentiment classification的综述:A Survey of Sentiment Analysis Based on Transfer Learning
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2019 一篇新survey:Transfer Adaptation Learning: A Decade Survey
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2018 一篇迁移度量学习的综述: Transfer Metric Learning: Algorithms, Applications and Outlooks
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2018 一篇最近的非对称情况下的异构迁移学习综述:Asymmetric Heterogeneous Transfer Learning: A Survey
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2018 Neural style transfer的一个survey:Neural Style Transfer: A Review
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2018 深度domain adaptation的一个综述:Deep Visual Domain Adaptation: A Survey
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2017 多任务学习的综述,来自香港科技大学杨强团队:A survey on multi-task learning
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2017 异构迁移学习的综述:A survey on heterogeneous transfer learning
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2017 跨领域数据识别的综述:Cross-dataset recognition: a survey
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2016 A survey of transfer learning。其中交代了一些比较经典的如同构、异构等学习方法代表性文章。
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2015 中文综述:迁移学习研究进展
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Survey on applications - 应用导向的综述:
- 视觉domain adaptation综述:Visual Domain Adaptation: A Survey of Recent Advances
- 迁移学习应用于行为识别综述:Transfer Learning for Activity Recognition: A Survey
- 迁移学习与增强学习:Transfer Learning for Reinforcement Learning Domains: A Survey
- 多个源域进行迁移的综述:A Survey of Multi-source Domain Adaptation。
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Data-Free Knowledge Transfer: A Survey
- A survey on data-free distillation and source-free DA
- 一篇关于data-free蒸馏和source-free DA的综述
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ICLR'24-spotlight Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [arxiv]
- Noisy model learning: fine-tuning to supress the bad effect of noisy pretraining data 通过使用轻量级finetune减少噪音预训练数据对下游任务的影响
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ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning [arxiv]
- Black-box foundation models for personalized federated learning 黑盒的blackbox模型进行个性化迁移学习
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IJCV'23 Exploring Vision-Language Models for Imbalanced Learning [arxiv] [code]
- Explore vision-language models for imbalanced learning 探索视觉大模型在不平衡问题上的表现
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ICCV'23 Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning [arxiv] [code]
- 达到对抗鲁棒性和泛化能力的trade off
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Towards Realistic Unsupervised Fine-tuning with CLIP [arxiv]
- Unsupervised fine-tuning of CLIP
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Improved OOD Generalization via Conditional Invariant Regularizer
- Improved OOD generalization via conditional invariant regularizer 通过条件不变正则进行OOD泛化
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An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications
- Information-theoretic analysis for transfer learning 用信息理论解释迁移学习
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PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners
- PAC-Bayesian domain adaptation 基于PAC-Bayesian的domain adaptation
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Optimal Representations for Covariate Shift
- Learning optimal representations for covariate shift
- 为covariate shift学习最优的表达
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NeurIPS-21 On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
- Theory and algorithm of domain-invariant learning for transfer learning
- 对invariant representation的理论和算法
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20210625 ICML-21 f-Domain-Adversarial Learning: Theory and Algorithms
- New theory based on f-divergence
- 基于f-divergence给出新的DA理论和算法
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20210521 When is invariance useful in an Out-of-Distribution Generalization problem ?
- When is invariant useful in OOD?
- 理论上分析了在OOD问题中invariance什么时候有用
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20200220 Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
- Noisy domain adaptation
- 用于噪声环境中的domain adaptation的方法
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20210127 A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
- 一个理论上更一般化的MMD差异用于领域自适应
- A more general MMD for domain adaptation
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20200615 Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks
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20200813 A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning
- OOD classifier for generalized zero-shot learning
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20200813 ICML-20 On Learning Language-Invariant Representations for Universal Machine Translation
- Theory for universal machine translation
- 对统一机器翻译模型进行了理论论证
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20200702 ICML-20 Few-shot domain adaptation by causal mechanism transfer
- The first work on causal transfer learning
- 日本理论组大佬Sugiyama的工作,causal transfer learning
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20191008 CVPR-19 Characterizing and Avoiding Negative Transfer
- Characterizing and avoid negative transfer
- 形式化并提出如何避免负迁移
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20190301 ALT-19 A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
- A new criterion for domain adaptation
- 提出一种新的可以强化domain adaptation表现的度量
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20181219 arXiv PAC Learning Guarantees Under Covariate Shift
- PAC learning theory for covariate shift
- Covariate shift问题的PAC学习理论
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20181206 arXiv Transferring Knowledge across Learning Processes
- Transfer learning across learning processes
- 学习过程中的知识迁移
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20181128 arXiv Theoretical Guarantees of Transfer Learning
- Some theoretical analysis of transfer learning
- 一些关于迁移学习的理论分析
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20181117 arXiv Theoretical Perspective of Deep Domain Adaptation
- Providing some theory analysis on deep domain adaptation
- 对deep domain adaptaiton做出了一些理论上的分析
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20181106 workshop GENERALIZATION BOUNDS FOR DOMAIN ADAPTATION VIA DOMAIN TRANSFORMATIONS
- Analyze some generalization bound for domain adaptation
- 对domain adaptation进行了一些理论上的分析
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20180724 arXiv Generalization Bounds for Unsupervised Cross-Domain Mapping with WGANs
- Provide a generalization bound for unsupervised WGAN in transfer learning
- 对迁移学习中无监督的WGAN进行了一些理论上的分析
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Rethinking Knowledge Transfer in Learning Using Privileged Information [arxiv]
- Using privileged information for knowledge transfer
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Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning [arxiv]
- Test-time prompt tuning for open classes
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SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning [arxiv]
- OOD fine-tuning for foundation models 大模型的OOD微调
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Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning [arxiv]
- Transfer learning for CSI-based positioning 用迁移学习进行基于CSI的定位
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CVPR'24 Exploring the Transferability of Visual Prompting for Multimodal Large Language Models [arxiv]
- Explore the transferability of visual prompting for multimodal LLM 探索多模态大模型visual prompt tuning的可迁移性
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MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks [arxiv]
- Model merge for medical transfer learning 通过模型合并进行医学迁移学习
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ICLR'24 扩展版 Learning with Noisy Foundation Models [arxiv]
- Fine-tune a noisy foundation model 基础模型有noisy的时候如何finetune
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Learning with Noisy Foundation Models [arxiv]
- Learning with noisy foundation models
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Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modeling [arxiv]
- Parameter-efficient adaptation for spatiotemporal modeling
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Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning? [arxiv]
- A comparison between visual prompt tuning and full finetuning 比较prompt tuning和全finetune
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ICLR'24 spotlight Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [arxiv]
- A new research direction of transfer learning in the era of foundation models 大模型时代一个新研究方向:研究预训练数据的噪声对下游任务影响
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NeurIPS'23 Geodesic Multi-Modal Mixup for Robust Fine-Tuning [paper]
- Geodesic mixup for robust fine-tuning
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NeurIPS'23 Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models [paper]
- Parameter and computation efficient transfer learning by reinforcement learning
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Equivariant Adaptation of Large Pre-Trained Models [arxiv]
- Equivariant adaptation of large pre-trained models 对大模型进行等边自适应
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Effective and Parameter-Efficient Reusing Fine-Tuned Models [arxiv]
- Effective and parameter-efficient reusing fine-tuned models 高效使用预训练模型
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Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [arxiv]
- Noisy model learning: fine-tuning to supress the bad effect of noisy pretraining data 通过使用轻量级finetune减少噪音预训练数据对下游任务的影响
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DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning [arxiv]
- Decomposed prompt tuning for parameter-efficient fine-tuning 基于分解prompt tuning的参数高效微调
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Towards Realistic Unsupervised Fine-tuning with CLIP [arxiv]
- Unsupervised fine-tuning of CLIP
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Fine-tuning can cripple your foundation model; preserving features may be the solution [arxiv]
- Fine-tuning will cripple foundation model
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Unified Transfer Learning Models for High-Dimensional Linear Regression [arxiv]
- Transfer learning for high-dimensional linar regression 迁移学习用于高维线性回归
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Review of Large Vision Models and Visual Prompt Engineering [arxiv]
- A survey of large vision model and prompt tuning 一个关于大视觉模型的prompt tuning的综述
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Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning [arxiv]
- A guide for parameter-efficient fine-tuning 一个对parameter efficient fine-tuning的全面介绍
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ICML'23 A Kernel-Based View of Language Model Fine-Tuning [arxiv]
- A kernel-based view of language model fine-tuning 一种以kernel的视角来看待fine-tuning的方法
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ICML'23 Improving Visual Prompt Tuning for Self-supervised Vision Transformers [arxiv]
- Improving visual prompt tuning for self-supervision 为自监督模型提高其 prompt tuning 表现
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Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting [arxiv]
- Using dynamic visual prompting for model adaptation 用动态视觉prompt进行模型适配
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ACL'23 Parameter-Efficient Fine-Tuning without Introducing New Latency [arxiv]
- Parameter-efficient finetuning 参数高效的finetune
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Ahead-of-Time P-Tuning [arxiv]
- Ahead-ot-time P-tuning for language models
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Parameter-Efficient Tuning Makes a Good Classification Head [arxiv]
- Parameter-efficient tuning makes a good classification head 参数高效的迁移学习成就一个好的分类头
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CVPR'23 Trainable Projected Gradient Method for Robust Fine-tuning [arxiv]
- Trainable PGD for robust fine-tuning 可训练的pgd用于鲁棒的微调技术
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ICLR'23 Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning [arxiv]
- Contrastive alignment for vision language models using transfer learning 使用参数高效迁移进行视觉语言模型的对比对齐
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ICLR'23 workshop SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models [arxiv]
- Sparse pre-training and dense fine-tuning
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A Unified Continual Learning Framework with General Parameter-Efficient Tuning [arxiv]
- A continual learning framework for parameter-efficient tuning 一个对于参数高效迁移的连续学习框架
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Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy [arxiv]
- Transfer learning for Depression detection 迁移学习用于脉动计焦虑检测
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ICLR'23 AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks [arxiv]
- GNN with autoML transfer learning 用于GNN的自动迁移学习
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Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm [arxiv]
- Parameter-efficient transfer learning: a two-stage approach 一种两阶段的参数高效迁移学习
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To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning [arxiv]
- Ensembling in transfer learning 调研迁移学习中的集成
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CVPR'13 Masked Images Are Counterfactual Samples for Robust Fine-tuning [arxiv]
- Masked images for robust fine-tuning 调研masked image对于fine-tuning的影响
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Finetune like you pretrain: Improved finetuning of zero-shot vision models [arxiv]]
- Improved fine-tuning of zero-shot models 针对zero-shot model提高fine-tuneing
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CVPR'22 Does Robustness on ImageNet Transfer to Downstream Tasks? [arxiv]
- Does robustness on imagenet transfer lto downstream tasks?
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NeurIPS'22 Improved Fine-Tuning by Better Leveraging Pre-Training Data [openreview]
- Using pre-training data for fine-tuning 用预训练数据来做微调
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On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [arxiv]
- Fine-tuned features for domain adaptation 微调的特征用于域自适应
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Transfer of Machine Learning Fairness across Domains [arxiv]
- Fairness transfer in transfer learning 迁移学习中的公平性迁移
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CVPR-20 Regularizing CNN Transfer Learning With Randomised Regression [arxiv]
- Using randomized regression to regularize CNN 用随机回归约束CNN迁移学习
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AAAI-21 TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning [arxiv]
- Pruning pre-trained model for transfer learning 通过对预训练模型进行剪枝来进行迁移学习
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Test-Time Training with Masked Autoencoders [arxiv]
- Test-time training with MAE MAE的测试时训练
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Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [arxiv]
- Test-time prompt tuning 测试时的prompt tuning
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TeST: test-time self-training under distribution shift [arxiv]
- Test-time self-training 测试时的self-training
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Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
- Parameter efficient CNN adapter for transfer learning 参数高效的CNN adapter用于迁移学习
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Hyper-Representations for Pre-Training and Transfer Learning
- Hyper-representation for pre-training and fine-tuning 对于预训练和微调的超表示
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Zero-Shot AutoML with Pretrained Models
- 用预训练模型进行零样本的自动机器学习
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How robust are pre-trained models to distribution shift?
- How robust are pre-trained models to distribution shift 评估预训练模型对于distribution shift的鲁棒性
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Wav2vec-S: Semi-Supervised Pre-Training for Speech Recognition
- Pretraining for speech recognition 用预训练模型进行语音识别
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ScholarBERT: Bigger is Not Always Better
- Empirical study on fine-tuning experiments 提出ScholarBERT进行大规模finetuning实验
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A Domain-adaptive Pre-training Approach for Language Bias Detection in News
- Domain-adaptive pre-training for language bias detection 领域适配预训练用于新闻语言偏见检测
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IJCAI-22 Parameter-Efficient Sparsity for Large Language Models Fine-Tuning
- Parameter-efficient sparsity for language model fine-tuning 参数高效的稀疏学习用于语言模型微调
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NAACL-22 Efficient Few-Shot Fine-Tuning for Opinion Summarization
- Few-shot fine-tuning for opinion summarization 小样本微调技术用于评论总结
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ACL-22 Probing Simile Knowledge from Pre-trained Language Models
- Probe simile knowledge from pre-trained model 从预训练模型中找出明喻知识
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Transfer Learning with Pre-trained Conditional Generative Models
- Transfer learning with pre-trained conditional generative models 条件生成模型用于迁移学习
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ICLR-22 Towards a Unified View of Parameter-Efficient Transfer Learning
- Unified view of parameter-efficient transfer learning 一个统一视角看待参数高效的迁移学习
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ICLR-22 Exploring the Limits of Large Scale Pre-training
- Many experiments to explore pre-training 许多实验来探索预训练
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Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
- Differential privacy by just fine-tune twice 通过微调两次进行差分隐私
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On Effectively Learning of Knowledge in Continual Pre-training
- Continual per-training 持续的预训练
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NAACL-22 GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering
- Fast fine-tuning for content-based collaborative filtering
- 快速的适用于协同过滤的微调
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- Finetuning in few-shot learning
- 小样本学习中的微调
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CVPR-22 Does Robustness on ImageNet Transfer to Downstream Tasks?
- Transfer learning robustness
- 迁移学习鲁棒性
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Blockchain as an Enabler for Transfer Learning in Smart Environments
- Blockchain transfer learning
- 用区块链进行迁移学习
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ICLR-22 Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
- Fin-tuning and linear probing for ood generalization
- 先linear probing最后一层再finetune对OOD任务最好
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A Broad Study of Pre-training for Domain Generalization and Adaptation
- A broad study of pre-training models for DA and DG
- 大量的实验进行DA和DG
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ACL-22 Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features
- Language-agnostic meta-learning for TTS
- 语言无关的元学习用于TTS
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Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models
- Adapt unfamiliar inputs to frozen pretrained models
- 让固定的预训练模型适配不熟悉的输入
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Pre-trained Token-replaced Detection Model as Few-shot Learner
- Pre-trained token-replaced detection model as few-shot learner
- 预训练的替换token的检测模型
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ICLR-22 spotlight Towards a Unified View of Parameter-Efficient Transfer Learning
- Detailed analysis of parameter-efficient transfer learning
- 对参数高效的迁移学习进行分析
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ICLR-22 BEiT: BERT Pre-Training of Image Transformers
- BERT pre-training of image transformers
- 用BERT的方式pre-train transformer
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Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice
- Using pre-training data to improve fine-tuning
- 使用预训练数据来帮助finetune
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An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification
- An ensemble of pre-trained transformer for malware classification
- 预训练的transformer通过集成进行恶意软件检测
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SLIP: Self-supervision meets Language-Image Pre-training
- Self-supervised learning + language image pretraining
- 用自监督学习用于语言到图像的预训练
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VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
- Vision-language efficient transfer learning
- 参数高校的vision-language任务迁移
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Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
- Revisit the transferability of supervised pretraining
- 重新思考有监督预训练的可迁移性
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NeurIPS-21 workshop Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift
- MMD for covariate shift
- 用MMD来解决covariate shift问题
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Combined Scaling for Zero-shot Transfer Learning
- Scaling up for zero-shot transfer learning
- 增大训练规模用于zero-shot迁移学习
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Improved Regularization and Robustness for Fine-tuning in Neural Networks
- Improve regularization and robustness for finetuning
- 针对finetune提高其正则和鲁棒性
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NeurIPS-21 Modular Gaussian Processes for Transfer Learning
- Modular Gaussian process for transfer learning
- 在迁移学习中使用modular Gaussian过程
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Rethinking supervised pre-training for better downstream transferring
- Rethink better finetune
- 重新思考预训练以便更好finetune
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EMNLP-21 Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
- Few-shot intent detection using pretrain and finetune
- 用迁移学习进行少样本意图检测
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- Using Kronecker decomposition and knowledge distillation for pre-trained language models compression
- 用Kronecker分解和知识蒸馏来进行语言模型的压缩
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How Does Adversarial Fine-Tuning Benefit BERT?
- Examine how does adversarial fine-tuning help BERT
- 探索对抗性finetune如何帮助BERT
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A Data Augmented Approach to Transfer Learning for Covid-19 Detection
- Data augmentation to transfer learning for COVID
- 迁移学习使用数据增强,用于COVID-19
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Finetuning Pretrained Transformers into Variational Autoencoders
- Finetune transformer to VAE
- 把transformer迁移到VAE
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Pre-trained Models for Sonar Images
- Pre-trained models for sonar images
- 针对声纳图像的预训练模型
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Domain Adaptor Networks for Hyperspectral Image Recognition
- Finetune for hyperspectral image recognition
- 针对高光谱图像识别的迁移学习
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CVPR-21 Efficient Conditional GAN Transfer With Knowledge Propagation Across Classes
- Transfer conditional GANs to unseen classes
- 通过知识传递,迁移预训练的conditional GAN到新类别
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20191011 arXiv Estimating Transfer Entropy via Copula Entropy
- Evaluate the transfer entopy via copula entropy
- 评估迁移熵
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20180925 arXiv DT-LET: Deep Transfer Learning by Exploring where to Transfer
- Explore the suitable layers to transfer
- 探索深度网络中效果表现好的对应的迁移层
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20200629 Transfer learning via L1 regulaziation
- Using L1 regularizationg for transfer learning
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20201016 Deep Ensembles for Low-Data Transfer Learning
- Deep ensemble models for transfer learning
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20210511 ACL-21 Are Pre-trained Convolutions Better than Pre-trained Transformers?
- Empirically investigate pre-trained convolutions and Transformers
- 设计实验探索预训练的卷积和Transformer的对比
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20190111 arXiv Transfer Representation Learning with TSK Fuzzy System
- Transfer learning with fuzzy system
- 基于模糊系统的迁移学习
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20201203 Pre-Trained Image Processing Transformer
- 用transformer做low-level的图像任务
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20200821 Self-Supervised Learning Across Domains
- 跨领域自监督学习
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20180403 arXiv 选择最优的子类生成方便迁移的特征:Class Subset Selection for Transfer Learning using Submodularity
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20180326 ICMLA-17 在类别不平衡情况下比较了一些迁移学习和传统方法的性能,并做出一些结论:Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance
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20190626 arXiv Transfer of Machine Learning Fairness across Domains
- Transfer of machine learning fairness across domains
- 机器学习的公平性的迁移
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20200210 WACVW-20 Impact of ImageNet Model Selection on Domain Adaptation
- A good experiment paper to indicate the power of representations
- 一篇很好的实验paper,揭示了深度特征+传统方法的有效性
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20191015 arXiv The Visual Task Adaptation Benchmark
- A new large benchmark for visual adaptation tasks by Google
- Google提出的一个巨大的视觉迁移任务数据集
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20190305 arXiv Let's Transfer Transformations of Shared Semantic Representations
- Transfer transformations from shared semantic representations
- 从共享的语义表示中进行特征迁移
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20190409 arXiv Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
- Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
- 通过可选择的CNN滤波器进行图像分类的fine-tuning
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20190111 arXiv Low-Cost Transfer Learning of Face Tasks
- Infer what task transfers better and how to transfer
- 探索对于一个预训练好的网络来说哪个任务适合迁移、如何迁移
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20191119 ICDM-19 Towards Making Deep Transfer Learning Never Hurt
- Towards making deep transfer learning never hurt
- 通过正则避免负迁移
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20181123 arXiv SpotTune: Transfer Learning through Adaptive Fine-tuning
- Very interesting work: how exactly determine the finetune process?
- 很有意思的工作:如何决定finetune的策略?
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20180425 arXiv 探索各个层对于迁移任务的作用,方便以后的迁移。比较有意思:CactusNets: Layer Applicability as a Metric for Transfer Learning
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传递迁移学习的第一篇文章,来自杨强团队,发表在KDD-15上:Transitive Transfer Learning
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AAAI-17 杨强团队最新的传递迁移学习:Distant Domain Transfer Learning
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20180819 LNCS-2018 Distant Domain Adaptation for Text Classification
- Propose a selected algorithm for distant domain text classification
- 提出一个用于远域的文本分类方法
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20190220 arXiv Fully-Featured Attribute Transfer
- Fully-featured image attribute transfer
- 图像特征迁移
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20190926 arXiv Transfer Learning across Languages from Someone Else's NMT Model
- Transfer learning across languages from NMT pretrained model
- 利用预训练好的NMT模型进行迁移学习
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20190929 NeurIPS-19 Deep Model Transferability from Attribution Maps
- Using attribution map for network similarity
- 与cvpr18的taskmony类似,这次用了属性图的方式探索网络的相似性
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20181121 arXiv An Efficient Transfer Learning Technique by Using Final Fully-Connected Layer Output Features of Deep Networks
- Using final fc layer to perform transfer learning
- 使用最后一层全连接层进行迁移学习
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ICML-14 著名的DeCAF特征:DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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Simultaneous Deep Transfer Across Domains and Tasks
- 发表在ICCV-15上,在传统深度迁移方法上又加了新东西
- 我的解读
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NeurIPS'22 Respecting Transfer Gap in Knowledge Distillation [arxiv]
- Transfer gap in distillation 知识蒸馏中的迁移gap
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Cross-Architecture Knowledge Distillation
- Cross-architecture knowledge distillation 跨架构的知识蒸馏
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ECCV-22 Knowledge Condensation Distillation
- Knowledge condensation distillation 知识压缩蒸馏
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FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
- Self-adaptive thresholding for semi-supervised learning 新的自适应阈值半监督方法
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TIP-22 Spot-adaptive Knowledge Distillation
- Spot-adaptive knowledge distillation 层次自适应的知识蒸馏
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CVPR-22 Decoupled Knowledge Distillation
- Decoupled knowledge distillation
- 解耦的知识蒸馏
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On Representation Knowledge Distillation for Graph Neural Networks
- Knowledge distillation for GNN
- 适用于GNN的知识蒸馏
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Estimating and Maximizing Mutual Information for Knowledge Distillation
- Global and local mutual information maximation for knowledge distillation
- 局部和全局互信息最大化用于蒸馏
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20210426 Distill on the Go: Online knowledge distillation in self-supervised learning
- Online knowledge distillation in self-supervised learning
- 自监督学习中的在线知识蒸馏
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20210202 ICLR-21 Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective
- Rethink soft labels for KD in a bias-variance tradeoff perspective
- 从偏差-方差的角度重新思考蒸馏中的软标签
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20200706 Interactive Knowledge Distillation
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20200412 ICML-19 Towards understanding knowledge distillation
- Some theoretical and empirical understanding to knowledge distllation
- 对知识蒸馏的一些理论和实验的分析
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20191202 AAAI-20 Towards Oracle Knowledge Distillation with Neural Architecture Search
- Using NAS for knowledge Distillation
- 用NAS帮助知识蒸馏
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20190401 arXiv Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
- Distill knowledge from BERT to simple neural networks
- 从BERT模型中迁移知识到简单网络中
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20181207 arXiv Feature Matters: A Stage-by-Stage Approach for Knowledge Transfer
- Feature transfer in student-teacher networks
- 在学生-教师网络中进行特征迁移
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20191204 AAAI-20 Online Knowledge Distillation with Diverse Peers
- Online Knowledge Distillation with Diverse Peers
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20191222 AAAI-20 Improved Knowledge Distillation via Teacher Assistant
- Teacher assistant helps knowledge distillation
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On Label Shift in Domain Adaptation via Wasserstein Distance
- Using Wasserstein distance to solve label shift in domain adaptation
- 在DA领域中用Wasserstein distance去解决label shift问题
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20210319 Cross-domain Activity Recognition via Substructural Optimal Transport | 知乎文章 | 微信公众号
- Using sub-structures for domain adaptation
- 采用子结构进行domain adaptation,比传统方法快5倍
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20210607 ICML-21 Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
- Sequential DA using distributionally robust experts
- 用鲁棒专家模型进行连续式领域自适应
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20200324 Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning
- Domain adaptation by class centroid matching and local manifold self-learning
- 集合了聚类、中心匹配,及自学习的DA
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20191204 arXiv Transferability versus Discriminability: Joint Probability Distribution Adaptation (JPDA)
- Joint adaptation with different weights
- 不同权重的联合概率适配
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20191125 AAAI-20 Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
- DA with selective pseudo label
- 结构化和选择性的伪标签用于DA
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20190703 arXiv Domain Adaptation via Low-Rank Basis Approximation
- Domain adaptation with low-rank basis approximation
- 低秩分解进行domain adaptation
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20190508 IJCNN-19 Unsupervised Domain Adaptation using Graph Transduction Games
- Domain adaptation using graph transduction games
- 用图转换博弈进行domain adaptation
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20190403 ICME-19 Easy Transfer Learning By Exploiting Intra-domain Structures Code
- An easy transfer learning approach with good performance
- 一个非常简单但效果很好的迁移方法
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20180724 ACMMM-18 Visual Domain Adaptation with Manifold Embedded Distribution Alignment
- The state-of-the-art results of domain adaptation, better than most traditional and deep methods
- 目前效果最好的非深度迁移学习方法,领先绝大多数最近的方法
- Code: MEDA
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20180912 arXiv Unsupervised Domain Adaptation Based on Source-guided Discrepancy
- Using source domain information to help domain adaptation
- 使用源领域数据辅助目标领域进行domain adaptation
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20181219 arXiv Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm
- Domain adaptation on graphs
- 在图上的领域自适应
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20181121 arXiv Deep Discriminative Learning for Unsupervised Domain Adaptation
- Deep discriminative learning for domain adaptation
- 同时进行源域和目标域上的分类判别
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20181114 arXiv Multiple Subspace Alignment Improves Domain Adaptation
- Project domains into multiple subsapce to do domain adaptation
- 将domain映射到多个subsapce上然后进行adaptation
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20180912 ICIP-18 Structural Domain Adaptation with Latent Graph Alignment
- Using graph alignment for domain adaptation
- 使用图对齐方式进行domain adaptation
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20180912 IEEE Access Unsupervised Domain Adaptation by Mapped Correlation Alignment
- Mapped correlation alignment for domain adaptation
- 用映射的关联对齐进行domain adaptation
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20180912 ICALIP-18 Domain Adaptation for Gaussian Process Classification
- Domain Adaptation for Gaussian Process Classification
- 在高斯过程分类中进行domain adaptation
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20180701 arXiv 对domain adaptation问题,基于optimal transport提出一种新的特征选择方法:Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
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20180510 IEEE Trans. Cybernetics 提出一个通用的迁移学习框架,对不同的domain进行不同的特征变换:Transfer Independently Together: A Generalized Framework for Domain Adaptation
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20180403 TIP-18 一篇传统方法做domain adaptation的文章,比很多深度方法结果都好:An Embarrassingly Simple Approach to Visual Domain Adaptation
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20180326 ICMLA-17 利用subsapce alignment进行迁移学习:Transfer Learning for Large Scale Data Using Subspace Alignment
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20180228 arXiv 一篇通过标签一致性和MMD准则进行domain adaptation的文章: Discriminative Label Consistent Domain Adaptation
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20180110 arXiv 一篇比较新的传统方法做domain adaptation的文章 Close Yet Discriminative Domain Adaptation
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20180105 arXiv 最优的贝叶斯迁移学习 Optimal Bayesian Transfer Learning
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20171201 ICCV-17 When Unsupervised Domain Adaptation Meets Tensor Representations
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201711 ICCV-17 Open set domain adaptation。
- 当source和target只共享某一些类别时,怎么处理?这个文章获得了ICCV 2017的Marr Prize Honorable Mention,值得好好研究。
- 我的解读
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201710 Domain Adaptation in Computer Vision Applications 里面收录了若干篇domain adaptation的文章,可以大概看看。
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学习迁移(Learning to Transfer, L2T)
- 迁移学习领域的新方向:与在线、增量学习结合
- 我的解读
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201708 Learning Invariant Riemannian Geometric Representations Using Deep Nets
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20170812 ICML-18 Learning To Transfer,将迁移学习和增量学习的思想结合起来,为迁移学习的发展开辟了一个崭新的研究方向。我的解读
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NIPS-17 JDOT: Joint distribution optimal transportation for domain adaptation
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JMLR-16 Distribution-Matching Embedding for Visual Domain Adaptation
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CoRR abs/1610.04420 (2016) Theoretical Analysis of Domain Adaptation with Optimal Transport
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CVPR-14 Transfer Joint Matching for Unsupervised Domain Adaptation
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ICCV-13 Transfer Feature Learning with Joint Distribution Adaptation
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迁移成分分析方法(Transfer component analysis, TCA)
- Domain adaptation via tranfer component analysis
- 发表在IEEE Trans. Neural Network期刊上(现改名为IEEE trans. Neural Network and Learning System),前作会议文章发在AAAI-09上
- 我的解读
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联合分布适配方法(joint distribution adaptation,JDA)
- Transfer Feature Learning with Joint Distribution Adaptation
- 发表在2013年的ICCV上
- 我的解读
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测地线流式核方法(Geodesic flow kernel, GFK)
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领域不变性迁移核学习(Transfer Kernel Learning, TKL)
- Domain invariant transfer kernel learning
- 发表在IEEE Trans. Knowledge and Data Engineering期刊上
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Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection [arxiv]
- Transfer learning from simulated to real scens for monocular 3D
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Multi-source Domain Adaptation for Panoramic Semantic Segmentation [arxiv]
- Multi-source domain adaptation for panoramic semantic segmentation
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A More Unified Theory of Transfer Learning [arxiv]
- More unified theory of transfer learning
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Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification [arxiv]
- Test-time adaptation for remote sensing image classification
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COD: Learning Conditional Invariant Representation for Domain Adaptation Regression [arxiv]
- Conditional invariant representation for domain adaptation regression
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Unsupervised Domain Adaption Harnessing Vision-Language Pre-training [arxiv]
- Domain adaptation using vision-language pre-training
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Can Modifying Data Address Graph Domain Adaptation? [arxiv]
- Alignment and rescaling for graph DA
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Improving Domain Adaptation Through Class Aware Frequency Transformation [arxiv]
- Class aware frequency transformation for domain adaptation
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Rethinking Domain Adaptation and Generalization in the Era of CLIP [arxiv]
- Rethinking domain adaptation in CLIP era
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Training-Free Model Merging for Multi-target Domain Adaptation [arxiv]
- Model merging for multi-target domain adaptation
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Multi-Task Domain Adaptation for Language Grounding with 3D Objects [arxiv]
- Multi-task domain adaptation for language grounding
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Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation [arxiv]
- Teacher-student framework for cross-domain adaptation 教师-学生框架进行跨领域适配
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MICCAI'24 MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection [arxiv]
- Adapting clip for few-shot medical image anomaly detection 对CLIP模型进行适配,以用于少样本图片异常检测
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MDDD: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition [arxiv]
- Manifold-based domain adaptation for EEG-based emotion recognition 基于流形的DA用于EEG情绪识别
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Domain Adaptation for Learned Image Compression with Supervised Adapters [arxiv]
- Domain adaptation for learned image compression DA用于图片压缩
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Test-Time Training on Graphs with Large Language Models (LLMs) [arxiv]
- Test-time training on graphs with LLMs 使用大语言模型在图上进行测试时训练
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DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series [arxiv]
- Domain adaptation for anomaly detection 使用域自适应进行时间序列异常检测
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CVPR'24 Unified Language-driven Zero-shot Domain Adaptation [arxiv]
- Language-driven zero-shot domain adaptation 语言驱动的零样本 DA
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CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning [arxiv]
- Chain-of-domain adaptation with visual prompt tuning 领域链adaptation
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Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [arxiv]
- Domain adaptation for eye-tracking systems 用DA进行眼球追踪
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SPA: A Graph Spectral Alignment Perspective for Domain Adaptation [NeurIPS 2023] [Pytorch]
- Graph spectral alignment and neighbor-aware propagation for domain adaptation
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Neurocomputing'24 Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation [arxiv]
- Unvertainty-aware source-free domain adaptation 基于不确定性伪标签的domain adaptation
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Efficient Domain Adaptation for Endoscopic Visual Odometry [arxiv]
- Efficient domain adaptation for visual odometry 高效DA用于odometry
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Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability [arxiv]
- Domain adaptation in ecology and hydrology 研究生态学和水文学中的DA
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ICLR'24 SF(DA)2: Source-free Domain Adaptation Through the Lens of Data Augmentation [arxiv]
- Source-free DA by data augmentation 通过数据增强来进行source-free DA
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CVPR'24 Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias [arxiv]
- Unviersal semi-supervised DA 通过公共类bias进行半监督DA
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Domain Adaptation Using Pseudo Labels for COVID-19 Detection [arxiv]
- Domain adaptation for COVID-19 detection 用DA进行covid-19检查
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Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans [arxiv]
- Covid-19 test using domain adaptation 使用集成和测试增强用于DA covid-19
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Addressing Source Scale Bias via Image Warping for Domain Adaptation [arxiv]
- Address the source scale bias for domain adaptation 解决源域的scale bias
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Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation [arxiv]
- Foundation models help domain adaptation 基础模型帮助领域自适应
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Unsupervised Domain Adaptation within Deep Foundation Latent Spaces [arxiv]
- Domain adaptation using foundation models
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LanDA: Language-Guided Multi-Source Domain Adaptation [arxiv]
- Language guided multi-source DA 在多源域自适应中使用语言指导
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AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space [arxiv]
- Semi-spuervised domain adaptation in the embedding space 在嵌入空间中进行半监督域自适应
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Inter-Domain Mixup for Semi-Supervised Domain Adaptation [arxiv]
- Inter-domain mixup for semi-supervised domain adaptation 跨领域mixup用于半监督域自适应
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Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation [arxiv]
- Source-free and image-only unsupervised domain adaptation
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Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition [arxiv]
- Multi-source DA with Transformer-based feature generation
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Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding [arxiv]
- Multi-modal domain adaptation 多模态领域自适应
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Domain Adaptive Graph Classification [arxiv]
- Domain adaptive graph classification 域适应的图分类
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Understanding and Estimating Domain Complexity Across Domains [arxiv]
- Understanding and estimating domain complexity 解释领域复杂性
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Prompt-based Domain Discrimination for Multi-source Time Series Domain Adaptation [arxiv]
- Prompt-based domain discrimination for time series domain adaptation 基于prompt的时间序列域自适应
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NeurIPS'23 SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models [arxiv]
- Test-time prompt adaptation for vision language models 对视觉-语言大模型的测试时prompt自适应
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DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation [arxiv]
- Dynamic adaptation for cross-domain few-shot segmentation 动态适配用于跨领域小样本分割
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A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting [arxiv]
- Instance weighting for domain adaptation 样本加权用于领域自适应
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Target-agnostic Source-free Domain Adaptation for Regression Tasks [arxiv]
- Target-agnostic source-free DA for regression 用于回归任务的source-free DA
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Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector [arxiv]
- Proposal-level unsupervised domain adaptation
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Better Practices for Domain Adaptation [arxiv]
- Better practice for domain adaptation
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Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images [arxiv]
- Domain adaptation for efficient ViT
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Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning [arxiv]
- Activity recognition using domain adaptation
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Unsupervised Domain Adaptation via Domain-Adaptive Diffusion [arxiv]
- Domain-adaptive diffusion for domain adaptation 领域自适应的diffusion
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SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation [arxiv]
- Using SAM for domain adaptation 使用segment anything进行domain adaptation
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Cross-Database and Cross-Channel ECG Arrhythmia Heartbeat Classification Based on Unsupervised Domain Adaptation [arxiv]
- EEG using unsupervised domain adaptation 用无监督DA来进行EEG心跳分类
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Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification [arxiv]
- Real-time online unsupervised domain adaptation for REID 无监督DA用于REID
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Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? A Metric for Unsupervised Evaluation of Domain Adaptation [arxiv]
- Evaluate domain adaptation models 评测domain adaptation的模型
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Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction [arxiv]
- Universal test-time adaptation
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Universal Domain Adaptation from Foundation Models [arxiv]
- Using foundation models for universal domain adaptation
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Multi-Source to Multi-Target Decentralized Federated Domain Adaptation [arxiv]
- Decentralized federated domain adaptation
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Multi-Source to Multi-Target Decentralized Federated Domain Adaptation [arxiv]
- Multi-source to multi-target federated domain adaptation 多源多目标的联邦域自适应
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ICML'23 AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation [arxiv]
- Adaptive test-time adaptation 非参数化分类器进行测试时adaptation
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CVPR'23 Zero-shot Generative Model Adaptation via Image-specific Prompt Learning [arxiv]
- Zero-shot generative model adaptation via image-specific prompt learning 零样本的生成模型adaptation
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Source-free Domain Adaptation Requires Penalized Diversity [arxiv]
- Source-free DA requires penalized diversity
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Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]
- Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习
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CVPR'23 Feature Alignment and Uniformity for Test Time Adaptation [arxiv]
- Feature alignment for test-time adaptation 使用特征对齐进行测试时adaptation
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TempT: Temporal consistency for Test-time adaptation [arxiv]
- Temporeal consistency for test-time adaptation 时间一致性用于test-time adaptation
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CVPR'23 A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation [arxiv]
- A new benchmark for domain adaptation 一个对于domain adaptation最新的benchmark
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Unsupervised domain adaptation by learning using privileged information [arxiv]
- Domain adaptation by privileged information 使用高级信息进行domain adaptation
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Probabilistic Domain Adaptation for Biomedical Image Segmentation [arxiv]
- Probabilistic domain adaptation for biomedical image segmentation 概率的domain adaptation用于生物医疗图像分割
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Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow [arxiv]
- Domain adaptation for foggy scene optical flow 领域自适应用于雾场景的光流
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Domain Adaptation for Time Series Under Feature and Label Shifts [arxiv]
- Domain adaptation for time series 用于时间序列的domain adaptation
-
TPAMI'23 Source-Free Unsupervised Domain Adaptation: A Survey [arxiv]
- A survey on source-free domain adaptation 关于source-free DA的一个最新综述
-
Discriminative Radial Domain Adaptation [arxiv]
- Discriminative radial domain adaptation 判别性的放射式domain adaptation
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WACV'23 Cross-Domain Video Anomaly Detection without Target Domain Adaptation [arxiv]
- Cross-domain video anomaly detection without target domain adaptation 跨域视频异常检测
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Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation [arxiv]
- Pre-trained models for source-free domain adaptation 用预训练模型进行source-free DA
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CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars [arxiv]
- Continual DA for self-driving cars 连续的domain adaptation用于自动驾驶
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Robust Mean Teacher for Continual and Gradual Test-Time Adaptation [arxiv]
- Mean teacher for test-time adaptation 在测试时用mean teacher进行适配
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ECCV-22 DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation [arXiv] [Code]
- Domain adaptation in semantic segmentation 语义分割域适应
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NeurIPS'22 Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [openreview]
- Adaptive contrastive learning for source-free DA 自适应的对比学习用于source-free DA
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NeurIPS'22 MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification [openreview]
- Multi-model domain adaptation mor medical image classification 多模型DA用于医疗数据
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NeurIPS'22 Domain Adaptation under Open Set Label Shift [openreview]
- Domain adaptation under open set label shift 在开放集的label shift中的DA
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NeurIPS'22 Test Time Adaptation via Conjugate Pseudo-labels [openreview]
- Test-time adaptation with conjugate pseudo-labels 用伪标签进行测试时adaptation
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On Fine-Tuned Deep Features for Unsupervised Domain Adaptation [arxiv]
- Fine-tuned features for domain adaptation 微调的特征用于域自适应
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WACV-23 ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing [arxiv]
- Domain adaptation for object detection using confidence mixing 用置信度mix做domain adaptation
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Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup [arxiv]
- Domain adaptation for COVID-19 用DA进行COVID-19预测
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ICONIP'22 IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels [arxiv]
- Intra-domain adaptation for segmentation 子领域对抗Adaptation
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NeurIPS'22 Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks [arxiv]
- Parameter-efficient multi-task adaptation 参数高效的多任务adaptation
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Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images [arxiv]
- Bomb craters detection using domain adaptation 用DA检测遥感图像中的炮弹弹坑
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WACV-23 TeST: Test-time Self-Training under Distribution Shift [arxiv]
- Test-time self-training 测试时训练
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Robust Domain Adaptation for Machine Reading Comprehension [arxiv]
- Domain adaptation for machine reading comprehension 机器阅读理解的domain adaptation
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IEEE-TMM'22 Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments [IEEE]
- Uncertainty modeling for domain adaptation 噪声环境下的domain adaptation
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MM-22 Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation
- Transformer for domain adaptation 用transformer进行DA
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NeurIPS-21 The balancing principle for parameter choice in distance-regularized domain adaptation
- Hyperparameter selection for domain adaptation 对adaptation中的正则项系数进行选择
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ECCV-22 Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
- Prototype continual domain adaptation 基于原型的类增量domain adaptation
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Transferability-Guided Cross-Domain Cross-Task Transfer Learning
- Cross-domain cross-task transfer learning 用迁移性指标指导跨领域跨任务迁移
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A Data-Based Perspective on Transfer Learning
- Analyze the data numbers in transfer learning 分析迁移学习中数据的重要性
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Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning
- Few-shot DA for unsupervised constrastive learning 小样本DA用于无监督对比学习
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ICPR-22 OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation
- Optimal transport-based domain adaptation 利用最优传输进行领域自适应
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CVPR-22 Safe Self-Refinement for Transformer-based Domain Adaptation
- Transformer-based domain adaptation 基于transformer的domain adaptation
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ISPASS-22 Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices
- Benchmarking test-time adaptation for edge devices
- 在端设备上评测test-time adaptation算法
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Multi-Source Domain Adaptation Based on Federated Knowledge Alignment
- Multi-source domain adaptation
- 多源域自适应
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A Broad Study of Pre-training for Domain Generalization and Adaptation
- A broad study of pre-training models for DA and DG
- 大量的实验进行DA和DG
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Open Set Domain Adaptation By Novel Class Discovery
- Open set DA by novel class discovery
- 基于新类发现的open set da
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ICML-21 workshop Domain Adaptation with Factorizable Joint Shift
- Domain adaptation with factorizable joint shift
- 基于可分解的联合漂移的领域自适应
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Causal Domain Adaptation with Copula Entropy based Conditional Independence Test
- Use copula entropy based conditional independence test for csusal domain adaptation
- 使用基于copula entopy的条件独立测试进行causal domain adaptation
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ICLR-22 Graph-Relational Domain Adaptation
- Graph-relational domain adapttion using topological structures
- 图级别的domain adaptation,使用拓扑结构
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UMAD: Universal Model Adaptation under Domain and Category Shift
- Model adaptation under domain and category shift
- 在domain和class都有shift的前提下进行模型适配
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A Survey of Unsupervised Domain Adaptation for Visual Recognition
- A new survey article of domain adaptation
- 对UDA的一个综述文章,来自作者博士论文
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Unsupervised Domain Adaptation: A Reality Check
- Doing experiments to show the progress of DA methods over the years
- 用大量的实验来验证近几年来DA方法的进展
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Hierarchical Optimal Transport for Unsupervised Domain Adaptation
- hierarchical optimal transport for UDA
- 层次性的最优传输用于domain adaptation
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Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning
- Using soft pseudo-label and curriculum learning to boost UDA
- 用软的伪标签和课程学习增强UDA方法
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WACV-22 Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation
- Sample-level self-distillation for semi-supervised DA
- 样本层次的自蒸馏用于半监督DA
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- Cross-modality domain adaptation for medical image segmentation
- 跨模态的DA用于医学图像分割
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Domain Adaptation for Rare Classes Augmented with Synthetic Samples
- Domain adaptation for rare class
- 稀疏类的domain adaptation
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BMVC-21 Dynamic Feature Alignment for Semi-supervised Domain Adaptation
- Dynamic feature alignment for semi-supervised DA
- 动态特征对齐用于半监督DA
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IEEE TIP-21 Joint Clustering and Discriminative Feature Alignment for Unsupervised Domain Adaptation
- Clustering and discriminative alignment for DA
- 聚类与判定式对齐用于DA
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IEEE TNNLS-21 Entropy Minimization Versus Diversity Maximization for Domain Adaptation
- Entropy minimization versus diversity max for DA
- 熵最小化与diversity最大化
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Cross-Region Domain Adaptation for Class-level Alignment
- Cross-region domain adaptation for class-level alignment
- 跨区域的领域自适应用于类级别的对齐
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EMNLP-21 Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
- UDA for machine translation
- 用领域自适应进行机器翻译
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- Domain adaptation for cross-modality liver segmentation
- 使用domain adaptation进行肝脏的跨模态分割
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CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
- Cross-domain transformer for domain adaptation
- 基于transformer进行domain adaptation
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Robust Ensembling Network for Unsupervised Domain Adaptation
- Ensembling network for domain adaptation
- 集成嵌入网络用于domain adaptation
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TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation
- Vision transformer for domain adaptation
- 用视觉transformer进行DA
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Learning Transferable Parameters for Unsupervised Domain Adaptation
- Learning partial transfer parameters for DA
- 学习适用于迁移部分的参数做UDA任务
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ICCV-21 BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation
- Bijective MMD for domain adaptation
- 双射MMD用于语义分割
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MM-21 Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
- Few-shot DA with image-to-class sparse similarity encoding
- 小样本的领域自适应
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Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning
- Prototype transfer and structure regularization
- 原型的迁移学习
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CVPR-21 Conditional Bures Metric for Domain Adaptation
- A new metric for domain adaptation
- 提出一个新的metric用于domain adaptation
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CVPR-21 Generalized Domain Adaptation
- A general definition for domain adaptation
- 一个更抽象更一般的domain adaptation定义
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CVPR-21 Reducing Domain Gap by Reducing Style Bias
- Syle-invariant training for adaptation and generalization
- 通过训练图像对style无法辨别来进行DA和DG
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20210706 CVPR-21 Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation
- Instance affinity learning for domain adaptation
- 样本间相似度学习,用于DA
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20210716 BMCV-extend Exploring Dropout Discriminator for Domain Adaptation
- Using multiple discriminators for domain adaptation
- 用分布估计代替点估计来做domain adaptation
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20201208 TIP Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation
- 用label propagation做半监督domain adaptation
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20201203 Unpaired Image-to-Image Translation via Latent Energy Transport
- 用能量模型做图像翻译
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20200927 Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy
- 加密情况下的MMD用于迁移学习
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20200914 A First Step Towards Distribution Invariant Regression Metrics
- 分布无关的回归评价
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20200813 ECCV-20 Learning to Cluster under Domain Shift
- Learning to cluster under domain shift
- 在domain shift的情况下进行聚类
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20200706 Learn Faster and Forget Slower via Fast and Stable Task Adaptation
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20200629 [ICML-20] Graph Optimal Transport for Cross-Domain Alignment
- Graph OT for cross-domain alignment
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20191202 AAAI-20 Stable Learning via Sample Reweighting
- Theoretical sample reweigting
- 理论和方法,用于sample reweight
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20191202 arXiv Domain-invariant Stereo Matching Networks
- Domain-invariant stereo matching networks
- 领域不变的匹配网络
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20191202 arXiv Learning Generalizable Representations via Diverse Supervision
- Diverse supervision helps to learn generalizable representations
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20191202 arXiv Domain-Aware Dynamic Networks
- Edge devices adaptative computing
- 边缘计算上的自适应计算
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20191029 Reducing Domain Gap via Style-Agnostic Networks
- Use style-agnostic networks to avoid domain gap
- 通过风格无关的网络来避免领域的gap
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20191008 arXiv DIVA: Domain Invariant Variational Autoencoders
- Domain invariant variational autoencoders
- 领域不变的变分自编码器
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20190821 arXiv Transfer Learning-Based Label Proportions Method with Data of Uncertainty
- Transfer learning with source and target having uncertainty
- 当source和target都有不确定label时进行迁移
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20190703 arXiv Inferred successor maps for better transfer learning
- Inferred successor maps for better transfer learning
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20190531 IJCAI-19 Adversarial Imitation Learning from Incomplete Demonstrations
- Adversarial imitation learning from imcomplete demonstrations
- 基于不完整实例的对抗模仿学习
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20190517 arXiv Budget-Aware Adapters for Multi-Domain Learning
- Budget-Aware Adapters for Multi-Domain Learning
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20190301 SysML-19 FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
- An efficient hardware for mobile computer vision applications using transfer learning
- 提出一个高效的用于移动计算机视觉应用的硬件
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20190118 arXiv Domain Adaptation for Structured Output via Discriminative Patch Representations
- Domain adaptation for structured output
- Domain adaptation用于结构化输出
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20181217 arXiv When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
- Combining semi-supervised learning and transfer learning
- 将半监督方法应用于迁移学习
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20181127 arXiv Privacy-preserving Transfer Learning for Knowledge Sharing
- First work on privacy preserving in transfer learning
- 探讨迁移学习中隐私保护的文章
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20181121 arXiv Not just a matter of semantics: the relationship between visual similarity and semantic similarity
- Interpreting relationships between visual similarity and semantic similarity
- 解释了视觉相似性和语义相似性的不同
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20181008 arXiv Unsupervised Learning via Meta-Learning
- Meta-learning for unsupervised learning
- 用于无监督学习的元学习
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20180919 JMLR Invariant Models for Causal Transfer Learning
- Invariant models for causal transfer learning
- 针对causal transfer learning提出不变模型
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20180912 arXiv Transfer Learning with Neural AutoML
- Applying transfer learning into autoML search
- 将迁移学习思想应用于automl
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20190904 arXiv On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
- Train binary classifiers from only unlabeled data
- 仅从无标记数据训练二分类器
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20180904 arXiv Learning Data-adaptive Nonparametric Kernels
- Learn a kernel that can do adaptation
- 学习一个可以自适应的kernel
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20180901 arXiv Distance Based Source Domain Selection for Sentiment Classification
- Propose a new domain selection method by combining existing distance functions
- 提出一种混合已有多种距离公式的源领域选择方法
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20180901 KBS Transfer subspace learning via low-rank and discriminative reconstruction matrix
- Transfer subspace learning via low-rank and discriminative reconstruction matrix
- 通过低秩和重构进行迁移学习
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20180825 arXiv Transfer Learning for Estimating Causal Effects using Neural Networks
- Using transfer learning for casual effect learning
- 用迁移学习进行因果推理
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20180724 ICPKR-18 Knowledge-based Transfer Learning Explanation
- Explain transfer learning things with some knowledge-based theory
- 用一些基于knowledge的方法解释迁移学习
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20180628 arXiv 提出Office数据集的实验室又放出一个数据集用于close set、open set、以及object detection的迁移学习:Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation
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20180604 arXiv 在Open set domain adaptation中,用共享和私有部分重建进行问题的解决:Learning Factorized Representations for Open-set Domain Adaptation
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20210706 CVPR-21 Multi-Target Domain Adaptation With Collaborative Consistency Learning
- Using collaborative consistency training for multi-target DA
- 用多个模型做集成一致性训练进行多目标DA
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20210625 CVPR-21 Generalized Domain Adaptation
- Generalized domain adaptation
- 更通用更一般的domain adaptation
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20210625 CVPR-21 A Fourier-based Framework for Domain Generalization
- Fourier based domain generalization
- 基于傅里叶特征的DG
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20210329 ICLR-21 Tent: Fully Test-Time Adaptation by Entropy Minimization
- Test time adaptation by entropy minimization
- 测试时通过熵最小化进行adaptation
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20210329 Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
- NAS for domain adaptation
- 用神经网络结构搜索做领域自适应
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20210312 Discrepancy-Based Active Learning for Domain Adaptation
- Discrepancy and active learning for DA
- 基于主动学习的DA
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20210312 Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
- Unbalanced minibatch OT for DA
- 非均衡的OT用于DA问题
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20210127 Hierarchical Domain Invariant Variational Auto-Encoding with weak domain supervision
- 利用VAE和解耦去做domain generalization
- Using VAE and disentanglement for domain generalization
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20201214 WWW-20 Domain Adaptation with Category Attention Network for Deep Sentiment Analysis
- Unify pivots and non-pivots, and provide interpretability for domain adaptation in sentiment analysis
- 统一pivots和non-pivots,并提供可解释性进行DA情感分析
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20201208 NIPS-20 Heuristic Domain Adaptation
- 启发式domain adaptation
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20200804 ECCV-20 spotlight Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
- 将现有的finetune机制进行扩展
- Extending finetune mechanism
- 20200804 ACMMM-20 Adversarial Bipartite Graph Learning for Video Domain Adaptation
- Video domain adaptation
- 视频的领域自适应
- 20200804 MICCAI-20 Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
- Generalizing across tasks, datasets, populations
- 在任务、数据集、人群之间做泛化
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20200724 Learning to Match Distributions for Domain Adaptation
- 自动深度迁移学习
- Automatic domain adaptation
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20200529 TNNLS Deep Subdomain Adaptation Network for Image Classification
- A fine-grained adaptation method with LMMD, which is very simple and effective
- 一种细粒度自适应的方法,使用LMMD进行对齐,该方法非常简单有效
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20200420 arXiv One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
- One-vs-rest deep model for open set recognition
- 用于开放集的识别的深度网络
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20200414 ICLR-20 Gradient as features for deep representation learning
- Gradients as features for deep representation learning on pretrained models
- 在预训练模型基础上,将梯度作为额外的feature,提高学习表现
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20200414 ICLR-20 Domain adaptive multi-branch networks
- A domain adaptation framework using a multi-branch cascade structure
- 一个用了多层级联、多分支结构的DA框架
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20200405 CVPR-20 Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
- A simple regularization-based adaptation method
- 一个非常简单的基于能量最小化的adaptation方法
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20200210 AAAI-20 Bi-Directional Generation for Unsupervised Domain Adaptation
- Bidirectional GANs for domain adaptation
- 双向的GAN用来做DA
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20191202 PR-19 Correlation-aware Adversarial Domain Adaptation and Generalization
- CORAL and adversarial for adaptation and generalization
- 基于CORAL和对抗网络的DA和DG
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20191201 BMVC-19 Domain Adaptation for Object Detection via Style Consistency
- Use style consistency for domain adaptation
- 通过结构一致性来进行domain adaptation
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20191124 AAAI-20 Knowledge Graph Transfer Network for Few-Shot Recognition
- GNN for semantic transfer for few-shot learning
- 用GNN进行类别的语义迁移用于few-shot learning
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20191124 arXiv Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
- Information bottleneck for unsupervised da
- 用了信息瓶颈来进行DA
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20191124 AAAI-20 (AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning)(https://arxiv.org/abs/1911.09659)
- Adaptively determine which layer to transfer or finetune
- 自适应地决定迁移哪个层或微调哪个层
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20191113 arXiv Knowledge Distillation for Incremental Learning in Semantic Segmentation
- Knowledge distillation for incremental learning in semantic segmentation
- 在语义分割问题中针对增量学习进行知识蒸馏
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20191111 NIPS-19 PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
- Multi-scale 3D DA network for point cloud representation
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20191111 CCIA-19 Feature discriminativity estimation in CNNs for transfer learning
- Feature discriminativity estimation in CNN for TL
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20191012 ICCV-19 Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
- Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
- 直接適應:學習非監督域自適應的判別功能
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20191015 arXiv Deep Kernel Transfer in Gaussian Processes for Few-shot Learning
- Deep kernel transfer learing in Gaussian process
- 高斯过程中的深度迁移学习
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20191008 EMNLP-19 workshop Domain Differential Adaptation for Neural Machine Translation
- Embrace the difference between domains for adaptation
- 拥抱domain的不同,并利用这些不同帮助adaptation
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20191008 BMVC-19 Multi-Weight Partial Domain Adaptation
- Class and sample weight contribution for partial domain adaptation
- 同时考虑类别和样本比重用于部分迁移学习
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20190813 ICCV-19 oral UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation
- A unified framework for domain adaptation
- 一个统一的用于domain adaptation的框架
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20190809 arXiv Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence
- Improve pseudo label confidence using multi-purposing DA
- 用多目标DA提高伪标签准确率
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20190809 arXiv Semi-supervised representation learning via dual autoencoders for domain adaptation
- Semi-supervised learning via autoencoders
- 半监督autoencoder用于DA
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20190809 arXiv Mind2Mind : transfer learning for GANs
- Transfer learning using GANs
- 用GAN进行迁移学习
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20190809 arXiv Self-supervised Domain Adaptation for Computer Vision Tasks
- Self-supervised DA
- 自监督DA
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20190809 arXiv Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation
- Hidden covariate shift
- 一种新的DA假设
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20190809 PR-19 Cross-domain Network Representations
- Cross-domain network representation learning
- 跨领域网络表达学习
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20190809 ICCV-19 Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
- Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
- 自适应的特征归一化用于DA
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20190731 MICCAI-19 Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
- Disentangled representations for unsupervised domain adaptation
- 基于解耦表征的无监督领域自适应
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20190719 arXiv Agile Domain Adaptation
- Domain adaptation by considering the difficulty in classification
- 通过考虑不同样本分离的难度进行domain adaptation
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20190718 arXiv Measuring the Transferability of Adversarial Examples
- Measure the transferability of adversarial examples
- 度量对抗样本的可迁移性
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20190604 IJCAI-19 DANE: Domain Adaptive Network Embedding
- Transfered network embeddings for different networks
- 不同网络表达的迁移
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20190604 arXiv Learning to Transfer: Unsupervised Meta Domain Translation
- Unsupervised meta domain translation
- 无监督领域翻译
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20190530 arXiv Learning Bregman Divergences
- Learning Bregman divergence
- 学习Bregman差异
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20190530 arXiv Adversarial Domain Adaptation Being Aware of Class Relationships
- Using class relationship for adversarial domain adaptation
- 利用类别关系进行对抗的domain adaptaition
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20190530 arXiv Cross-Domain Transferability of Adversarial Perturbations
- Cross-Domain Transferability of Adversarial Perturbations
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20190525 PAMI-19 Learning More Universal Representations for Transfer-Learning
- Learning more universal representations for transfer learning
- 对迁移学习设计2种方式学到更通用的表达
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20190517 ICML-19 Learning What and Where to Transfer
- Learning what and where to transfer in deep networks
- 学习深度网络从何处迁移
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20190517 ICML-19 Zero-Shot Voice Style Transfer with Only Autoencoder Loss
- Zero-shot voice style transfer with only autoencoder loss
- 零次声音迁移
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20190515 CVPR-19 Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
- Domain adaptation for object detection
- 领域自适应用于物体检测
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20190507 NAACL-HLT 19 Transfer of Adversarial Robustness Between Perturbation Types
- Transfer of Adversarial Robustness Between Perturbation Types
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20190416 arXiv ACE: Adapting to Changing Environments for Semantic Segmentation
- Propose a new method that can adapt to new environments
- 提出一种可以适配不同环境的方法
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20190416 arXiv Incremental multi-domain learning with network latent tensor factorization
- Incremental multi-domain learning with network latent tensor factorization
- 网络隐性tensor分解应用于多领域增量学习
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20190415 PAKDD-19 Parameter Transfer Unit for Deep Neural Networks
- Propose a parameter transfer unit for DNN
- 对深度网络提出参数迁移单元
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20190412 PAMI-19 Beyond Sharing Weights for Deep Domain Adaptation
- Domain adaptation by not sharing weights
- 通过不共享权重来进行domain adaptation
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20190405 IJCNN-19 Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
- The first work to accelerate transfer learning
- 第一个加速迁移学习的工作
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20190102 WSDM-19 Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
- Reinforced transfer learning for deep text matching
- 迁移学习进行深度文本匹配
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20190102 arXiv DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
- Adversarial + residual for domain adaptation
- 对抗+残差进行domain adaptation
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20181220 arXiv TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation
- Two weighted inconsistency-reduced networks for partial domain adaptation
- 两个权重网络用于部分domain adaptation
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20181127 arXiv Learning Grouped Convolution for Efficient Domain Adaptation
- Group convolution for domain adaptation
- 群体卷积进行domain adaptation
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20181121 arXiv Unsupervised Domain Adaptation: An Adaptive Feature Norm Approach
- A nonparametric method for domain adaptation
- 一种无参数的domain adaptation方法
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20181121 arXiv Domain Adaptive Transfer Learning with Specialist Models
- Sample reweighting methods for domain adaptative
- 样本权重更新法进行domain adaptation
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20180926 ICLR-18 Self-ensembling for visual domain adaptation
- Self-ensembling for domain adaptation
- 将self-ensembling应用于da
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20180620 CVPR-18 用迁移学习进行fine tune:Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
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20180321 CVPR-18 构建了一个迁移学习算法,用于解决跨数据集之间的person-reidenfication: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
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20180315 ICLR-17 一篇综合进行two-sample stest的文章:Revisiting Classifier Two-Sample Tests
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20171214 arXiv Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications
- 在实验中探索了数据量多少,和相似度这两个因素对迁移学习效果的影响
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NIPS-17 Learning Multiple Tasks with Multilinear Relationship Networks
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20210420 arXiv On Universal Black-Box Domain Adaptation
- Universal black-box domain adaptation
- 黑盒情况下的universal domain adaptation
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20210319 Learning Invariant Representations across Domains and Tasks
- Automatically learn to match distributions
- 自动适配分布的任务适配网络
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20191222 arXiv Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
- Generate data without priors for transfer learning based on deep dream
- 只用网络架构不用原来数据,生成新数据用于迁移
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20191201 arXiv A Unified Framework for Lifelong Learning in Deep Neural Networks
- A unified framework for life-long learing in DNN
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20191201 arXiv ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
- Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
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20191119 NIPS-19 workshop Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
- Ensemble DA using noise labels
- 在ensemble中出现noise label时如何处理
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20191029 KBS Semi-supervised representation learning via dual autoencoders for domain adaptation
- Semi-supervised domain adaptation with autoencoders
- 用自动编码器进行半监督的DA
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20190926 arXiv Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment
- Use class-conditional DA for domain adaptation
- 使用类条件对齐进行domain adaptation
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20190926 arXiv A Deep Learning-Based Approach for Measuring the Domain Similarity of Persian Texts
- Deep learning based domain similarity learning
- 利用深度学习进行领域相似度的学习
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20190926 arXiv FEED: Feature-level Ensemble for Knowledge Distillation
- Feature-level knowledge distillation
- 特征层面的知识蒸馏
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20190926 ICCV-19 Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification
- A simple approach for domain adaptation
- 一个很简单的DA方法
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20190910 BMVC-19 Curriculum based Dropout Discriminator for Domain Adaptation
- Curriculum dropout for domain adaptation
- 基于课程学习的dropout用于DA
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20190909 PAMI Inferring Latent Domains for Unsupervised Deep Domain Adaptation
- Inferring latent domains for unsupervised deep domain
- 在深度迁移学习中推断隐含领域
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20190729 ICCV workshop Multi-level Domain Adaptive learning for Cross-Domain Detection
- Multi-level domain adaptation for cross-domain Detection
- 多层次的domain adaptation
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20190626 IJCAI-19 Bayesian Uncertainty Matching for Unsupervised Domain Adaptation
- Bayesian uncertainty matching for da
- 贝叶斯网络用于da
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20190419 CVPR-19 DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition
- Dual-Domain LSTM for Cross-Dataset Action Recognition
- 跨数据集的动作识别
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20190109 InfSc Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
- Extension of Central Moment Discrepancy (ICLR-17) approach
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20181212 ICONIP-18 Domain Adaptation via Identical Distribution Across Models and Tasks
- Transfer from large net to small net
- 从大网络迁移到小网络
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20181212 AIKP Deep Domain Adaptation
- Low-rank + deep nn for domain adaptation
- Low-rank用于深度迁移
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20181211 arXiv Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models
- Transfer learning with deep generative model
- 通过深度生成模型进行迁移学习
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20181121 arXiv Integrating domain knowledge: using hierarchies to improve deep classifiers
- Using hierarchies to help deep learning
- 借助于层次关系来帮助深度网络进行学习
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20181117 arXiv AdapterNet - learning input transformation for domain adaptation
- Learning input transformation for domain adaptation
- 对domain adaptation任务学习输入的自适应
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20181115 AAAI-19 Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
- Local domain alignment for domain adaptation
- 局部领域自适应
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20181115 NIPS-18 Co-regularized Alignment for Unsupervised Domain Adaptation
- The idea is the same with the above one...
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20181113 NIPS-18 Generalized Zero-Shot Learning with Deep Calibration Network
- Deep calibration network for zero-shot learning
- 提出deep calibration network进行zero-shot learning
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20181110 AAAI-19 Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
- Transfer learning for bounding neuron activation boundaries
- 使用迁移学习进行神经元激活边界判定
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20181108 arXiv Deep feature transfer between localization and segmentation tasks
- Feature transfer between localization and segmentation
- 在定位与分割任务间进行迁移
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20181107 BigData-18 Transfer learning for time series classification
- First work on deep transfer learning for time series classification
- 第一个将深度迁移学习用于时间序列分类
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20181106 PRCV-18 Deep Local Descriptors with Domain Adaptation
- Adding MMD layers to conv and fc layers
- 在卷积和全连接层都加入MMD
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20181106 LNCS-18 LSTN: Latent Subspace Transfer Network for Unsupervised Domain Adaptation
- Combine subspace learning and neural network for DA
- 将子空间表示和深度网络结合起来用于DA
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20181105 SIGGRAPI-18 Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis
- Representation learning for cross-domains
- 跨领域的特征学习
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20181105 arXiv Progressive Memory Banks for Incremental Domain Adaptation
- Progressive memory bank in RNN for incremental DA
- 针对增量的domain adaptation,进行记忆单元的RNN学习
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20180901 arXiv Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation
- deep domain adaptation + intra-class / inter-class distance
- 深度domain adaptation再加上类内类间距离学习
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20180819 arXiv Conceptual Domain Adaptation Using Deep Learning
- A search framework for deep transfer learning
- 提出一个可以搜索的framework进行迁移学习
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20180731 ECCV-18 DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
- Deep + Joint distribution adaptation + optimal transport
- 深度 + 联合分布适配 + optimal transport
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20180731 ICLR-18 Few Shot Learning with Simplex
- Represent deep learning using the simplex
- 用单纯性表征深度学习
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20180724 AIAI-18 Improving Deep Models of Person Re-identification for Cross-Dataset Usage
- apply deep models to cross-dataset RE-ID
- 将深度迁移学习应用于跨数据集的Re-ID
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20180724 ECCV-18 Zero-Shot Deep Domain Adaptation
- Perform zero-shot domain adaptation when there is no target domain data available
- 当目标领域的数据不可用时如何进行domain adaptation :
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20180724 ICCSE-18 Deep Transfer Learning for Cross-domain Activity Recognition
- Provide source domain selection and activity recognition for cross-domain activity recognition
- 提出了跨领域行为识别中的深度方法模型,以及相关的领域选择方法
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20180530 arXiv 用于深度网络的鲁棒性domain adaptation方法:Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
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20180522 arXiv 用CNN进行跨领域的属性学习:Cross-domain attribute representation based on convolutional neural network
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20180428 CVPR-18 相互协同学习:Deep Mutual Learning
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20180428 ICLR-18 自集成学习用于domain adaptation:Self-ensembling for visual domain adaptation
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20180428 IJCAI-18 将knowledge distilation用于transfer learning,然后进行视频分类:Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification
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20180426 arXiv 深度学习中的参数如何进行迁移?(杨强团队):Parameter Transfer Unit for Deep Neural Networks
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20180425 CVPR-18(oral) 对不同的视觉任务进行建模,从而可以进行深层次的transfer:Taskonomy: Disentangling Task Transfer Learning
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20180410 ICLR-17 第一篇用可变RNN进行多维时间序列迁移的文章:Variational Recurrent Adversarial Deep Domain Adaptation
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20180403 arXiv 本地和云端CNN迁移融合的图片分类:Hierarchical Transfer Convolutional Neural Networks for Image Classification
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20180402 CVPR-18 渐进式domain adaptation:Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
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20180329 arXiv 基于attention机制的多任务学习:End-to-End Multi-Task Learning with Attention
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20180326 arXiv 将迁移学习用于Faster R-CNN对象识别中:Domain Adaptive Faster R-CNN for Object Detection in the Wild
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20180326 Pattern Recognition-17 多标签迁移学习方法应用于脸部属性分类:Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification
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20180326 类似于ResNet的思想,在传统layer的ReLU之前加一个additive layer进行domain adaptation,思想简洁,效果非常好:Layer-wise domain correction for unsupervised domain adaptation
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20180326 Pattern Recognition-17 基于Batch normalization提出了AdaBN,很简单:Adaptive Batch Normalization for practical domain adaptation
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20180309 arXiv 利用已有网络的先验知识来加速目标网络的训练:Transfer Automatic Machine Learning
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2018 ICLR-18 最小熵领域对齐方法 Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation code
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ICLR-17 Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
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ICCV-17 CCSA: Unified Deep Supervised Domain Adaptation and Generalization
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ICML-17 JAN: Deep Transfer Learning with Joint Adaptation Networks
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2017 Google: Learning Transferable Architectures for Scalable Image Recognition
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NIPS-16 RTN: Unsupervised Domain Adaptation with Residual Transfer Networks
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CoRR abs/1603.04779 (2016) AdaBN: Revisiting batch normalization for practical domain adaptation
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JMLR-16 DANN: Domain-adversarial training of neural networks
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20171226 NIPS 2016 把传统工作搬到深度网络中的范例:不是只学习domain之间的共同feature,还学习每个domain specific的feature。这篇文章写得非常清楚,通俗易懂! Domain Separation Networks | 代码
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20171222 ICCV 2017 对于target中只有很少量的标记数据,用深度网络结合孪生网络的思想进行泛化:Unified Deep Supervised Domain Adaptation and Generalization | 代码和数据
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20171126 NIPS-17 Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
- 李飞飞小组发在NIPS 2017的文章。针对不同的domain、不同的feature、不同的label space,统一学习一个深度网络进行表征。
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201711 一个很好的深度学习+迁移学习的实践教程,有代码有数据,可以直接上手:基于深度学习和迁移学习的识花实践
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ECCV-16 Deep CORAL: Correlation Alignment for Deep Domain Adaptation
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ECCV-16 DRCN: Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
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ICML-15 DAN: Learning Transferable Features with Deep Adaptation Networks
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ICML-15 GRL: Unsupervised Domain Adaptation by Backpropagation
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NIPS-14 How transferable are features in deep neural networks?
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CoRR abs/1412.3474 (2014) Deep Domain Confusion(DDC): Maximizing for Domain Invariance
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深度联合适配网络(Joint Adaptation Network, JAN)
- Deep Transfer Learning with Joint Adaptation Networks
- 延续了之前的DAN工作,这次考虑联合适配
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20191214 arXiv Learning Domain Adaptive Features with Unlabeled Domain Bridges
- Learning domain adaptive features with unlabeled CycleGAN
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20191214 AAAI-20 Adversarial Domain Adaptation with Domain Mixup
- Domain adaptation with data mixup
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20190916 arXiv Compound Domain Adaptation in an Open World
- Domain adaptation using the target domain knowledge
- 使用目标域的知识来进行domain adaptation
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20101008 ICCV-19 Enhancing Adversarial Example Transferability with an Intermediate Level Attack
- Enhancing adversarial examples transerability
- 增强对抗样本的可迁移性
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20190408 arXiv DeceptionNet: Network-Driven Domain Randomization
- Using only source data for domain randomization
- 仅利用源域数据进行domain randomization
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20190220 arXiv Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks
- Domain adaptation using deep learning with cross-grafted stacks
- 用跨领域嫁接栈进行domain adaptation
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20181217 arXiv DLOW: Domain Flow for Adaptation and Generalization
- Domain flow for adaptation and generalization
- 域流方法应用于领域自适应和扩展
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20181212 arXiv Learning Transferable Adversarial Examples via Ghost Networks
- Use ghost networks to learn transferrable adversarial examples
- 使用ghost网络来学习可迁移的对抗样本
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20181205 arXiv Unsupervised Domain Adaptation using Generative Models and Self-ensembling
- UDA using CycleGAN
- 基于CycleGAN的domain adaptation
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20181205 arXiv VADRA: Visual Adversarial Domain Randomization and Augmentation
- Domain randomization and augmentation
- Domain randomization和增强
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20181128 arXiv Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
- CycleGAN for domain adaptation
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20181127 arXiv Distorting Neural Representations to Generate Highly Transferable Adversarial Examples
- Generate transferrable examples to fool networks
- 生成一些可迁移的对抗样本来迷惑神经网络,在各个网络上都表现好
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20181123 arXiv Progressive Feature Alignment for Unsupervised Domain Adaptation
- Progressively selecting confident pseudo labeled samples for transfer
- 渐进式选择置信度高的伪标记进行迁移
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20181113 NIPS-18 Conditional Adversarial Domain Adaptation
- Using conditional GAN for domain adaptation
- 用conditional GAN进行domain adaptation
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20181107 NIPS-18 Invariant Representations without Adversarial Training
- Get invariant representations without adversarial training
- 不进行对抗训练获得不变特征表达
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20181105 arXiv Efficient Multi-Domain Dictionary Learning with GANs
- Dictionary learning for multi-domains using GAN
- 用GAN进行多个domain的字典学习
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20181012 arXiv Domain Confusion with Self Ensembling for Unsupervised Adaptation
- Domain confusion and self-ensembling for DA
- 用于Domain adaptation的confusion和self-ensembling方法
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20180912 arXiv Improving Adversarial Discriminative Domain Adaptation
- Improve ADDA using source domain labels
- 提高ADDA方法的精度,使用source domain的label
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20180731 ECCV-18 Dist-GAN: An Improved GAN using Distance Constraints
- Embed an autoencoder in GAN to improve its stability in training and propose two distances
- 将autoencoder集成到GAN中,提出相应的两种距离进行度量,提高了GAN的稳定性
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20180724 ECCV-18 Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
- Using stacked CycleGAN to perform image-to-image translation
- 用stacked cycleGAN进行image-to-image的翻译
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20180628 ICML-18 Pixel-level和feature-level的domain adaptation:CyCADA: Cycle-Consistent Adversarial Domain Adaptation
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20180619 CVPR-18 将optimal transport加入adversarial中进行domain adaptation:Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation
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20180616 CVPR-18 用GAN进行domain adaptation:Generate To Adapt: Aligning Domains using Generative Adversarial Networks
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20180612 ICML-18 利用多个数据集辅助,从而提高目标领域的学习能力:RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
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20180612 ICML-18 利用GAN进行多个domain的联合分布优化:JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
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20180605 arXiv NAM: Non-Adversarial Unsupervised Domain Mapping
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20180508 arXiv 利用GAN,从有限数据中生成另一个domain的数据:Transferring GANs: generating images from limited data
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20180501 arXiv Open set domain adaptation的对抗网络版本:Open Set Domain Adaptation by Backpropagation
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20180427 arXiv 提出了adversarial residual的概念,进行深度对抗迁移:Unsupervised Domain Adaptation with Adversarial Residual Transform Networks
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20180424 CVPR-18 用GAN和迁移学习进行图像增强:Adversarial Feature Augmentation for Unsupervised Domain Adaptation
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20180413 arXiv 一种思想非常简单的深度迁移方法,仅考虑进行domain之间的类别概率矫正就能取得非常好的效果:Simple Domain Adaptation with Class Prediction Uncertainty Alignment
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20180413 arXiv Mingming Gong提出的用因果生成网络进行深度迁移:Causal Generative Domain Adaptation Networks
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20180410 CVPR-18(oral) 用两个分类器进行对抗迁移:Maximum Classifier Discrepancy for Unsupervised Domain Adaptation 代码
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20180403 CVPR-18 将样本权重应用于对抗partial transfer中:[Importance Weighted Adversarial Nets for Partial Domain Adaptation](https://arxiv.org/abs/1803.09210
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20180326 MLSP-17 把domain separation network和对抗结合起来,提出了一个对抗网络进行迁移学习:Adversarial domain separation and adaptation
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20180326 ICIP-17 类似于domain separation network,加入了对抗判别训练,同时优化分类、判别、相似度三个loss:Semi-supervised domain adaptation via convolutional neural network
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20180116 ICLR-18 用对偶的形式替代对抗训练中原始问题的表达,从而进行分布对齐 Stable Distribution Alignment using the Dual of the Adversarial Distance
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20180111 arXiv 在GAN中用原始问题的对偶问题替换max问题,使得梯度更好收敛 Stable Distribution Alignment Using the Dual of the Adversarial Distance
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20180110 AAAI-18 将Wasserstein GAN用到domain adaptaiton中 Wasserstein Distance Guided Representation Learning for Domain Adaptation
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201707 CVPR-17 Adversarial Representation Learning For Domain Adaptation
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ICCV-17 CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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ICCV-17 DualGAN: DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
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CVPR-17 Asymmetric Tri-training for Unsupervised Domain Adaptation
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ICML-17 DiscoGAN: Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
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TKDE-22 Generalizing to Unseen Domains: A Survey on Domain Generalization | 知乎文章 | 微信公众号 | Code
- First survey on domain generalization
- 第一篇对Domain generalization (领域泛化)的综述
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Federated Domain Generalization: A Survey [arxiv]
- A survey on federated domain generalization 一篇关于联邦域泛化的综述
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KDD 2023 tutorial: trustworthy machine learning: robustness, generalization, and interpretability [link]
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WSDM-23 and IJCAI-22 A tutorial on domain generalization [link] | [website]
- A tutorial on domain generalization
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Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation [arxiv]
- Online test-time adaptation using dual-path adversarial lifting
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Domain penalisation for improved Out-of-Distribution Generalisation [arxiv]
- OOD using domain penalization
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Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift [arxiv]
- Domain generalization under invariant feature shift
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Reducing Spurious Correlation for Federated Domain Generalization [arxiv]
- Federated domain generalization by reducing spurious correlation
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DGMamba: Domain Generalization via Generalized State Space Model [arXiv]
- Domain generalization using mamba 用Mamba结构进行DG
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ICASSP'24 Learning Inference-Time Drift Sensor-Actuator for Domain Generalization [IEEE]
- Inference-time drift actuator for OOD generalization
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ICASSP'24 SBM: Smoothness-Based Minimization for Domain Generalization [IEEE]
- Smoothness-based minimization for OOD generalization
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ICASSP'24 G2G: Generalized Learning by Cross-Domain Knowledge Transfer for Federated Domain Generalization [IEEE]
- Federated domain generalization
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ICASSP'24 Single-Source Domain Generalization in Fundus Image Segmentation Via Moderating and Interpolating Input Space Augmentation [IEEE]
- Single-source DG in fundus image segmentation
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ICASSP'24 Style Factorization: Explore Diverse Style Variation for Domain Generalization [IEEE]
- Style variation for domain generalization
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ICASSP'24 SPDG-Net: Semantics Preserving Domain Augmentation through Style Interpolation for Multi-Source Domain Generalization [IEEE]
- Domain augmentation for multi-source DG
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ICASSP'24 Domaindiff: Boost out-of-Distribution Generalization with Synthetic Data [IEEE]
- Using synthetic data for OOD generalization
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ICASSP'24 Multi-Level Augmentation Consistency Learning and Sample Selection for Semi-Supervised Domain Generalization [IEEE]
- Multi-level augmentation for semi-supervised domain generalization
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ICASSP'24 MMS: Morphology-Mixup Stylized Data Generation for Single Domain Generalization in Medical Image Segmentation [IEEE]
- Morphology-mixup for domain generalization
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On the Benefits of Over-parameterization for Out-of-Distribution Generalization [arxiv]
- Over-parameterazation for OOD generalizaiton 分析了过参数化对OOD的影响
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EAGLE: A Domain Generalization Framework for AI-generated Text Detection [arxiv]
- Domain generalization for AI content detection 用DG进行AI生成内容检测
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DPStyler: Dynamic PromptStyler for Source-Free Domain Generalization [arxiv]
- Dynamic propmtstyler for source-free DG 动态prompt分格化用于source-free DG
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V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions [arxiv]
- DG for multi-agent perception 领域泛化用于极端天气
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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification [arxiv]
- Bidirectional multi-step DG for REID 双向领域泛化用于REID
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ICASSP'24 Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning [arxiv]
- Test-time distribution learning adapter
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A Study on Domain Generalization for Failure Detection through Human Reactions in HRI [arxiv]
- Domain generalization for failure detection through human reactions in HRI
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ICLR'24 Towards Robust Out-of-Distribution Generalization Bounds via Sharpness [arxiv]
- Robust OOD generalization bounds
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Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy [arxiv]
- OOD detection for ablated learned temperature energy
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ICLR'24 Supervised Knowledge Makes Large Language Models Better In-context Learners [arxiv]
- Small models help large language models for better OOD 用小模型帮助大模型进行更好的OOD
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NeurIPS'23 Test-Time Distribution Normalization for Contrastively Learned Visual-language Models [paper]
- Test-time distribution normalization for contrastively learned VLM
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NeurIPS'23 A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP) [paper]
- A fine-gained analysis of CLIP robustness
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NeurIPS'23 CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation [arxiv]
- Open set domain generalization using extra classes
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CPAL'24 FIXED: Frustratingly Easy Domain Generalization with Mixup [arxiv]
- Easy domain generalization with mixup
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SDM'24 Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution [arxiv]
- Optimization and model selection for domain generalization
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Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation [arxiv]
- SAM for single-source domain generalization
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Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features [arxiv]
- Open domain generalization with a single network 用单一网络结构进行开放式domain generalizaition
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Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation [arxiv]
- Using vision foundation models for domain genealized semantic segmentation 用视觉基础模型进行域泛化语义分割
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On the Out-Of-Distribution Robustness of Self-Supervised Representation Learning for Phonocardiogram Signals [arxiv]
- OOD robustness for self-supervised learning for phonocardiogram 心音图信号自监督的OOD鲁棒性
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A2XP: Towards Private Domain Generalization [arxiv]
- Private domain generalization 隐私保护的domain generalization
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Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation [arxiv]
- Auto-weighting for test-time adaptation 自动权重的TTA
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Domain Generalization by Learning from Privileged Medical Imaging Information [arxiv]
- Domain generalizaiton by learning from privileged medical imageing inforamtion
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SSL-DG: Rethinking and Fusing Semi-supervised Learning and Domain Generalization in Medical Image Segmentation [arxiv]
- Semi-supervised learning + domain generalization 把半监督和领域泛化结合在一起
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WACV'24 Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization [arxiv]
- Class and domain augmentation for single-source open-domain DG 结合类和domain增强做单源DG
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Robust Fine-Tuning of Vision-Language Models for Domain Generalization [arxiv]
- Robust fine-tuning for domain generalization 用于领域泛化的鲁棒微调
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NeurIPS 2023 Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models [arxiv]
- Distill OOD robustness from vision-language foundational models 从VLM模型中蒸馏出OOD鲁棒性
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UbiComp 2024 Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition [arxiv]
- Test-time adaptation for activity recognition 测试时adaptation用于行为识别
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Prompting-based Efficient Temporal Domain Generalization [arxiv]
- Prompt based temporal domain generalization 基于prompt的时间域domain generalization
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Domain Generalization with Fourier Transform and Soft Thresholding [arxiv]
- Domain generalization with Fourier transform 基于傅里叶变换和软阈值进行domain generalization
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Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization [arxiv]
- Multi-scale and multi-layer contrastive learning for DG 多尺度和多层对比学习用于DG
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Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy [arxiv]
- Domain generalization for diabetic retinopathy 用领域泛化进行糖尿病视网膜病
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NormAUG: Normalization-guided Augmentation for Domain Generalization [arxiv]
- Normalization augmentation for domain generalization
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Benchmarking Algorithms for Federated Domain Generalization [arxiv]
- Benchmark algorthms for federated domain generalization 对联邦域泛化算法进行的benchmark
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DISPEL: Domain Generalization via Domain-Specific Liberating [arxiv]
- Domain generalization via domain-specific liberating
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Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization [arxiv]
- Exemplar-based style synthesis for domain generalization 样例格式合成用于DG
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Pruning for Better Domain Generalizability [arxiv]
- Using pruning for better domain generalization 使用剪枝操作进行domain generalization
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TMLR'23 Generalizability of Adversarial Robustness Under Distribution Shifts [openreview]
- Evaluate the OOD perormance of adversarial training 评测对抗训练模型的OOD鲁棒性
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Domain Generalization for Domain-Linked Classes [arxiv]
- Domain generalization for domain-linked classes
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Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup [arxiv]
- Why mixup works for domain generalization? 系统性研究为啥mixup对OOD很work
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Improved Test-Time Adaptation for Domain Generalization [arxiv]
- Improved test-time adaptation for domain generalization
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Reweighted Mixup for Subpopulation Shift [arxiv]
- Reweighted mixup for subpopulation shift
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Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation [arxiv]
- Domain generalization for medical segmentation 用domain generalization进行医学分割
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CVPR'23 Meta-causal Learning for Single Domain Generalization [arxiv]
- Meta-causal learning for domain generalization
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Domain Generalization In Robust Invariant Representation [arxiv]
- Domain generalization in robust invariant representation
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Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness [arxiv]
- Local structure preserving for adversarial robustness 通过保留局部结构来进行对抗鲁棒性
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TFS-ViT: Token-Level Feature Stylization for Domain Generalization [arxiv]
- Token-level feature stylization for domain generalization 用token-level特征变换进行domain generalization
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Are Data-driven Explanations Robust against Out-of-distribution Data? [arxiv]
- Data-driven explanations robust? 探索数据驱动的解释是否是OOD鲁棒的
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ERM++: An Improved Baseline for Domain Generalization [arxiv]
- Improved ERM for domain generalization 提高的ERM用于domain generalization
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Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]
- Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习
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CVPR'23 TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [arxiv]
- Improve generalization and adversarial robustness 同时提高鲁棒性和泛化性
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Finding Competence Regions in Domain Generalization [arxiv]
- Finding competence regions in domain generalization 在DG中发现能力区域
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CVPR'23 ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization [arxiv]
- A lightweight module for domain generalization 一个用于DG的轻量级模块
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CVPR'23 Sharpness-Aware Gradient Matching for Domain Generalization [arxiv]
- Sharpness-aware gradient matching for DG 利用梯度匹配进行domain generalization
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Domain Generalization via Nuclear Norm Regularization [arxiv]
- Domain generalization via nuclear norm regularization 使用核归一化进行domain generalization
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Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology [arxiv]
- Imbalanced domain generalization for single cell classification 不平衡的DG用于单细胞分类
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FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning [arxiv]
- Fast generalization for federated CLIP 在联邦中进行快速的CLIP训练
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Robust Representation Learning with Self-Distillation for Domain Generalization [arxiv]
- Robust representation learning with self-distillation
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ICLR-23 Temporal Coherent Test-Time Optimization for Robust Video Classification [arxiv]
- Temporal distribution shift in video classification
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On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective [arxiv] | [code]
- Adversarial and OOD evaluation of ChatGPT 对ChatGPT鲁棒性的评测
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How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? [arxiv]
- Regression models uncertainty for distribution shift 回归模型对于分布漂移的不确定性
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ICLR'23 SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning [arxiv]
- Semi-supervised learning algorithm 解决标签质量问题的半监督学习方法
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Empirical Study on Optimizer Selection for Out-of-Distribution Generalization [arxiv]
- Opimizer selection for OOD generalization OOD泛化中的学习器选择
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ICML'22 Understanding the failure modes of out-of-distribution generalization [arxiv]
- Understand the failure modes of OOD generalization 探索OOD泛化中的失败现象
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ICLR'23 Out-of-distribution Representation Learning for Time Series Classification [arxiv]
- OOD for time series classification 时间序列分类的OOD算法
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CLIP the Gap: A Single Domain Generalization Approach for Object Detection [arxiv]
- Using CLIP for domain generalization object detection 使用CLIP进行域泛化的目标检测
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TMLR'22 A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges [openreview]
- A recent survey on OOD/anomaly detection 一篇最新的关于OOD/anomaly detection的综述
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NeurIPS'18 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks [paper]
- Using class-conditional distribution for OOD detection 使用类条件概率进行OOD检测
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ICLR'22 Discrete Representations Strengthen Vision Transformer Robustness [arxiv]
- Embed discrete representation for OOD generalization 在ViT中加入离散表征增强OOD性能
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Learning to Learn Domain-invariant Parameters for Domain Generalization [[arxiv](Learning to Learn Domain-invariant Parameters for Domain Generalization)]
- Learning to learn domain-invariant parameters for DG 元学习进行domain generalization
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HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization [arxiv]
- Hypernetwork-based ensembling for domain generalization 超网络构成的集成学习用于domain generalization
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The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [arxiv]
- OOD using fine-tuning 系统总结了基于fine-tuning进行OOD的一些结果
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GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective [arxiv]
- OOD for natural language processing evaluation 提出GLUE-X用于OOD在NLP数据上的评估
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CVPR'22 Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts [arxiv]
- Vision transformers generalization under distribution shifts 评估ViT的分布漂移
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NeurIPS'22 Models Out of Line: A Fourier Lens on Distribution Shift Robustness [arxiv]
- A fourier lens on distribution shift robustness 通过傅里叶视角来看分布漂移的鲁棒性
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Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [arxiv]
- Normalization perturbation for domain generalization 通过归一化扰动来进行domain generalization
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FIXED: Frustraitingly easy domain generalization using Mixup [arxiv]
- 使用Mixup进行domain generalization
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Learning to Learn Domain-invariant Parameters for Domain Generalization [arxiv]
- Learning to learn domain-invariant parameters for domain generalization
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NeurIPS'22 LOG: Active Model Adaptation for Label-Efficient OOD Generalization [openreview]
- Model adaptation for label-efficient OOD generalization
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NeurIPS'22 Domain Generalization without Excess Empirical Risk [openreview]
- Domain generalization without excess empirical risk
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NeurIPS'22 FedSR: A Simple and Effective Domain Generalization Method for Federated Learning [openreview]
- FedSR for federated learning domain generalization 用于联邦学习的domain generalization
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NeurIPS'22 Probable Domain Generalization via Quantile Risk Minimization [openreview]
- Domain generalization with quantile risk minimization 用quantile风险最小化的domain generalization
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NeurIPS'22 Your Out-of-Distribution Detection Method is Not Robust! [openreview]
- OOD models are not robust 分布外泛化模型不够鲁棒
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PhDthesis Generalizing in the Real World with Representation Learning [arxiv]
- A phd thesis about generalization in real world 一篇关于现实世界如何做Generalization的博士论文
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The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning [arxiv]
- Evolution of OOD robustness by fine-tuning
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Out-of-Distribution Generalization in Algorithmic Reasoning Through Curriculum Learning [arxiv]
- OOD in algorithmic reasoning 算法reasoning过程中的OOD
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Towards Out-of-Distribution Adversarial Robustness [arxiv]
- OOD adversarial robustness OOD对抗鲁棒性
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TripleE: Easy Domain Generalization via Episodic Replay [arxiv]
- Easy domain generalization by episodic replay
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Deep Spatial Domain Generalization [arxiv]
- Deep spatial domain generalization
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Assaying Out-Of-Distribution Generalization in Transfer Learning [arXiv]
- A lot of experiments to show OOD performance
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ICML-21 Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [arxiv]
- Strong correlation between ID and OOD
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Generalized representations learning for time series classification[arxiv]
- OOD for time series classification 域泛化用于时间序列分类
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Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification [arxiv]
- Domain generalization for cross-scene hyperspectral image classification 域泛化用于高光谱图像分类
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Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation arxiv
- OOD by frequency-based augmentation 通过基于频率的数据增强进行OOD
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Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study arxiv
- Domain generalizationfor prostate segmentation 领域泛化用于前列腺分割
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Domain Adaptation from Scratch arxiv
- Domain adaptation from scratch
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Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution arxiv
- Model selection for domain generalization 域泛化中的模型选择问题
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Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
- Equivariant disentangled transformation for domain generalization 新的建模domain generalization的思路
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ECCV-22 workshop Domain-Specific Risk Minimization
- Domain-specific risk minization for OOD 领域特异性风险最小化用于域泛化
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IJCAI-22 Domain Generalization through the Lens of Angular Invariance
- Using angular invariance for domain generalization 使用角度不变性进行domain generalization
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Adaptive Domain Generalization via Online Disagreement Minimization
- Online domain generalization via disagreement minimization 在线DG
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Self-Distilled Vision Transformer for Domain Generalization
- Vision transformer for domain generalization 用ViT做domain generalization
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TMLR-22 Domain-invariant Feature Exploration for Domain Generalization
- Exploring domain-invariant feature for domain generalization 探索领域不变特征在领域泛化中的应用
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TIST-22 Domain Generalization for Activity Recognition via Adaptive Feature Fusion
- Domain generalization for activity recognition 领域泛化用于行为识别
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The Importance of Background Information for Out of Distribution Generalization
- Background information for OOD generalization 背景信息对于OOD泛化的重要性
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Causal Balancing for Domain Generalization
- Causal balancing for domain generalization 因果平衡用于领域泛化
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Temporal Domain Generalization with Drift-Aware Dynamic Neural Network
- Temporal domain generalization with drift-aware dynamic neural network 时序域泛化
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Multiple Domain Causal Networks
- Mlutiple domain causal networks 多领域的因果网络
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IJCAI-21 Test-time Fourier Style Calibration for Domain Generalization
- Test-time calibration for domain generalization 用傅立叶变化进行域泛化的测试时矫正
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Out-Of-Distribution Detection In Unsupervised Continual Learning
- OOD detection in unsupervised continual learning 无监督持续学习中进行OOD检测
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ICLR-22 Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
- Fin-tuning and linear probing for ood generalization
- 先linear probing最后一层再finetune对OOD任务最好
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ICLR-22 Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks
- Asymmetry learning for OOD tasks
- 非对称学习用于OOD任务
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Improving Generalization in Federated Learning by Seeking Flat Minima
- Seeking flat minima for domain generalization in federated learning
- 通过寻找平坦值进行联邦学习领域泛化
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Gated Domain-Invariant Feature Disentanglement for Domain Generalizable Object Detection
- Channel masking for domain generalization object detection
- 通过一个gate控制channel masking进行object detection DG
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A Broad Study of Pre-training for Domain Generalization and Adaptation
- A broad study of pre-training models for DA and DG
- 大量的实验进行DA和DG
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Learning Semantic Segmentation from Multiple Datasets with Label Shifts
- Learning semantic segmentation from many datasets with label shifts
- 在有标签漂移的情况下从多个数据集中学习语义分割
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PAKDD-22 Layer Adaptive Deep Neural Networks for Out-of-distribution Detection
- Layer adaptive network for OOD detection
- 层自适应的网络进行OOD检测
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ICLR-22 oral A Fine-Grained Analysis on Distribution Shift
- Extensive experiments on distribution shift for OOD
- 大量的实验进行OOD验证
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ICLR-22 oral Fine-Tuning Distorts Pretrained Features and Underperforms Out-of-Distribution
- Fine-tuning with linear probing for OOD
- 微调加上linear probing用于OOD
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ICLR-22 Uncertainty Modeling for Out-of-Distribution Generalization
- Uncertainty modeling for OOD generalization
- 用于分布外泛化的不确定性建模
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TKDE-22 Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
- Adaptiev memory network for anomaly detection
- 自适应的记忆网络用于异常检测
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ICIP-22 Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
- Meta-learning for domain generalization
- 元学习用于domain generalization
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ICIP-22 Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
- Few-shot generalization using meta-learning
- 用元学习进行小样本的泛化
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More is Better: A Novel Multi-view Framework for Domain Generalization
- Multi-view learning for domain generalization
- 使用多视图学习来进行domain generalization
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Unsupervised Domain Generalization by Learning a Bridge Across Domains
- Unsupervised domain generalization
- 无监督的domain generalization
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ROBIN : A Benchmark for Robustness to Individual Nuisancesin Real-World Out-of-Distribution Shifts
- A benchmark for robustness to individual OOD
- 一个OOD的benchmark
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ICML-21 workshop Towards Principled Disentanglement for Domain Generalization
- Principled disentanglement for domain generalization
- Principled解耦用于domain generalization
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Federated Learning with Domain Generalization
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Federated domain generalization
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联邦学习+domain generalization
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Semi-Supervised Domain Generalization in Real World:New Benchmark and Strong Baseline
- Semi-supervised domain generalization
- 半监督+domain generalization
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MICCAI-21 Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
- Domain generalization for mammography detection
- 领域泛化用于乳房X射线检查
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- Domain generalization by audio-visual alignment
- 通过音频-视频对齐进行domain generalization
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Dynamically Decoding Source Domain Knowledge For Unseen Domain Generalization
- Ensemble learning for domain generalization
- 用集成学习进行domain generalization
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Scale Invariant Domain Generalization Image Recapture Detection
- Scale invariant domain generalizaiton
- 尺度不变的domain generalization
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ICCV-21 Shape-Biased Domain Generalization via Shock Graph Embeddings
- Domain generalization based on shape information
- 基于形状进行domain generalization
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Domain and Content Adaptive Convolution for Domain Generalization in Medical Image Segmentation
- Domain generalization for medical image segmentation
- 领域泛化用于医学图像分割
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Fishr: Invariant Gradient Variances for Out-of-distribution Generalization
- Invariant gradient variances for OOD generalization
- 不变梯度方差,用于OOD
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Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization
- Domain generalization with wasserstein DRO
- 使用Wasserstein DRO进行domain generalization
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CIKM-21 AdaRNN: Adaptive Learning and Forecasting of Time Series Code 知乎文章 Video
- A new perspective to using transfer learning for time series analysis
- 一种新的建模时间序列的迁移学习视角
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20190531 arXiv Image Alignment in Unseen Domains via Domain Deep Generalization
- Deep domain generalization for image alignment
- 深度领域泛化用于图像对齐
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20200821 ECCV-20 Towards Recognizing Unseen Categories in Unseen Domains
- Recognizing unseen classes in unseen domains
- 对未知领域识别未知类
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20200706 ICLR-21 In Search of Lost Domain Generalization
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20201016 Energy-based Out-of-distribution Detection
- Energy-based OOD
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20201222 AAAI-21 DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
- OOD generalization
- 用特征分解和语义增强做OOD泛化
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20210106 Style Normalization and Restitution for Domain Generalization and Adaptation
- Style normalization and restitution for DA and DG
- 风格归一化用于DA和DG任务
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CVPR-21 Uncertainty-Guided Model Generalization to Unseen Domains
- Uncertainty-guided generalization
- 基于不确定性的domain generalization
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CVPR-21 Adaptive Methods for Real-World Domain Generalization
- Adaptive methods for domain generalization
- 动态算法,用于domain generalization
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20180701 arXiv 做迁移时,只用source数据,不用target数据训练:Generalizing to Unseen Domains via Adversarial Data Augmentation
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201711 ICLR-18 GENERALIZING ACROSS DOMAINS VIA CROSS-GRADIENT TRAINING
- 不同于以往的工作,本文运用贝叶斯网络建模label和domain的依赖关系,抓住training、inference 两个过程,有效引入domain perturbation来实现domain adaptation。
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ICLR-18 generalizing across domains via cross-gradient training
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20181106 PRCV-18 Domain Attention Model for Domain Generalization in Object Detection
- Adding attention for domain generalization
- 在domain generalization中加入了attention机制
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20181225 WACV-19 Multi-component Image Translation for Deep Domain Generalization
- Using GAN generated images for domain generalization
- 用GAN生成的图像进行domain generalization
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20180724 arXiv Domain Generalization via Conditional Invariant Representation
- Using Conditional Invariant Representation for domain generalization
- 生成条件不变的特征表达,用于domain generalization问题
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20181212 arXiv Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models
- Domain generalization method
- 一种针对于unseen domain的学习方法
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20171210 AAAI-18 Learning to Generalize: Meta-Learning for Domain Generalization
- 将Meta-Learning与domain generalization结合的文章,可以联系到近期较为流行的few-shot learning进行下一步思考。
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NeurIPS'23 When Visual Prompt Tuning Meets Source-Free Domain Adaptive Semantic Segmentation [paper]
- Source-free domain adaptation using visual prompt tuning
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PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization [arxiv]
- Prompt-driven style generation for source-free domain generalization
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Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [arxiv]
- Source-free domain adaptation for MRI analysis
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ICCV'23 Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation [arxiv]
- Domain-specificity for source-free DA 用领域特异性驱动的source-free DA
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Visual Prompt Tuning for Test-time Domain Adaptation [arxiv]
- VPT for test-time adaptation 用prompt tuning进行test-time DA
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Active Source Free Domain Adaptation
- Active source-free DA 主动学习-无源域DA
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- Source-free domain adaptation using constrastive learning
- 无源域数据的DA,利用对比学习
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20200629 ICML-20 Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation - Source-free adaptation - 在adaptation过程中不访问source data
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Open-Set Crowdsourcing using Multiple-Source Transfer Learning
- Open-set crowdsourcing using multiple-source transfer learning
- 使用多源迁移进行开放集的crowdsourcing
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BMVC-21 Domain Attention Consistency for Multi-Source Domain Adaptation
- Multi-source domain adaptation using attention consistency
- 用attention一致性进行多源的domain adaptation
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CVPR-21 Wasserstein Barycenter for Multi-Source Domain Adaptation
- Use Wasserstein Barycenter for multi-source domain adaptation
- 利用Wasserstein Barycenter进行DA
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20210430 Graphical Modeling for Multi-Source Domain Adaptation
- Graphical models for multi-source DA
- 用概率图模型进行多源领域自适应
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20210430 Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
- ReID using multi-source DA
- 用多源领域自适应进行ReID任务
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20200427 TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
- A cycle-gan style multi-source DA
- 类似于cyclegan的多源领域适应
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20190902 AAAI-19 Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources
- Multi-source domain adaptation using both features and classifier adaptation
- 利用特征和分类器同时适配进行多源迁移,效果很好
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20181212 AIKP Multi-source Transfer Learning
- Multi-source transfer
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20181207 arXiv Moment Matching for Multi-Source Domain Adaptation
- Moment matching and propose a new large dataset for domain adaptation
- 提出一种moment matching的网络,并且提出一种新的domain adaptation数据集,很大
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CoRR abs/1711.09020 (2017) StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
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20180524 arXiv 探索了Multi-source迁移学习的一些理论:Algorithms and Theory for Multiple-Source Adaptation
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20181117 AAAI-19 Robust Optimization over Multiple Domains
- Optimization on multi domains
- 针对多个domain建模并优化
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20180912 arXiv Multi-target Unsupervised Domain Adaptation without Exactly Shared Categories
- Multi-target domain adaptation
- 多目标的domain adaptation
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20180316 arXiv 用optimal transport解决domain adaptation中类别不平衡的问题:Optimal Transport for Multi-source Domain Adaptation under Target Shift
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20190717 AAAI Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding
- Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding
- 异构迁移学习中用对抗核嵌入的深度矩阵
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20190829 ACMMM-19 Heterogeneous Domain Adaptation via Soft Transfer Network
- Soft-mmd loss in heterogeneous domain adaptation
- 异构迁移学习中用soft-mmd loss
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20181113 ACML-18 Unsupervised Heterogeneous Domain Adaptation with Sparse Feature Transformation
- Heterogeneous domain adaptation
- 异构domain adaptation
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20180901 TKDE A General Domain Specific Feature Transfer Framework for Hybrid Domain Adaptation
- Hybrid DA: special case in Heterogeneous DA
- 提出一种新的混合DA问题和方法
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20180606 arXiv 一篇最近的对非对称情况下的异构迁移学习综述:Asymmetric Heterogeneous Transfer Learning: A Survey
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20180403 Neural Processing Letters-18 异构迁移学习:Label Space Embedding of Manifold Alignment for Domain Adaption
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20180105 arXiv 异构迁移学习 Heterogeneous transfer learning
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CVPR-22 workshop Online Unsupervised Domain Adaptation for Person Re-identification
- Online domain adaptation for REID 在线adaptation
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Mixture of basis for interpretable continual learning with distribution shifts
- Incremental learning with mixture of basis
- 用mixture of domains进行增量学习
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20180326 考虑主动获取label的budget情况下的在线迁移学习:Online domain adaptation by exploiting labeled features and pro-active learning
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20180128 第一篇在线迁移学习的文章,发表在ICML-10上,系统性地定义了在线迁移学习的任务,给出了进行在线同构和异构迁移学习的两种学习模式。Online Transfer Learning
- 扩充的期刊文章发在2014年的AIJ上:Online Transfer Learning
- 我的解读
- 文章代码:OTL
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20180126 两篇在线迁移学习:
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20180126 TKDE-17 同时有多个同构和异构源域时的在线迁移学习:Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources
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KIS-17 Online transfer learning by leveraging multiple source domains 提出一种综合衡量多个源域进行在线迁移学习的方法。文章的related work是很不错的survey。
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CIKM-13 OMS-TL: A Framework of Online Multiple Source Transfer Learning 第一次在mulitple source上做online transfer,也是用的分类器集成。
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ICLR-17 ONLINE BAYESIAN TRANSFER LEARNING FOR SEQUENTIAL DATA MODELING 用贝叶斯的方法学习在线的HMM迁移学习模型,并应用于行为识别、睡眠监测,以及未来流量分析。
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KDD-14 Scalable Hands-Free Transfer Learning for Online Advertising 提出一种无参数的SGD方法,预测广告量
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TNNLS-17 Online Feature Transformation Learning for Cross-Domain Object Category Recognition 在线feature transformation方法
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ICPR-12 Online Transfer Boosting for Object Tracking 在线transfer 样本
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TKDE-14 Online Feature Selection and Its Applications 在线特征选择
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AAAI-15 Online Transfer Learning in Reinforcement Learning Domains 应用于强化学习的在线迁移学习
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AAAI-15 Online Boosting Algorithms for Anytime Transfer and Multitask Learning 一种通用的在线迁移学习方法,可以适配在现有方法的后面
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IJSR-13 Knowledge Transfer Using Cost Sensitive Online Learning Classification 探索在线迁移方法,用样本cost
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Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning
- Few-shot DA for unsupervised constrastive learning 小样本DA用于无监督对比学习
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Interpretable Concept-based Prototypical Networks for Few-Shot Learning
- Concept-based prototypical network for few-shot learning
- 基于概念的原型网络用于小样本学习
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How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?
- Self-supervised learning for cross-domain few-shot
- 自监督用于跨领域小样本
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20181128 arXiv One Shot Domain Adaptation for Person Re-Identification
- One shot learning for REID
- One shot for再识别
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20210426 Few-shot Continual Learning: a Brain-inspired Approach
- Few-shot continual learning
- 小样本持续学习
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20201203 How to fine-tune deep neural networks in few-shot learning?
- 对few-shot任务如何fine-tune深度网络?
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20201116 Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks
- 量子神经网络中的finetune
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20200608 ICML-20 Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
- Using domain adaptation to solve the few-shot learning
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20200408 ICLR-20 A Baseline for Few-Shot Image Classification - A simple finetune+entropy minimization approach with strong baseline - 一个微调+最小化熵的小样本学习方法,结果很强
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20200405 ICCV-19 Variational few-shot learning
- Variational few-shot learning
- 变分小样本学习
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20200405 ICLR-20 A baseline for few-shot image classification
- A simple but powerful baseline for few-shot image classification
- 一个简单但是很有效的few-shot baseline
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20200324 IEEE TNNLS Few-Shot Learning with Geometric Constraints
- Few-shot learning with geometric constraints
- 用了一些几何约束进行小样本学习
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20190813 arXiv Domain-Specific Embedding Network for Zero-Shot Recognition
- Domain-specific embedding network for zero-shot learning
- 领域自适应的zero-shot learning
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20190401 TIp-19 Few-Shot Deep Adversarial Learning for Video-based Person Re-identification
- Few-shot deep adversarial learning
- Few-shot对抗学习
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20190305 arXiv Zero-Shot Task Transfer
- Zero-shot task transfer
- Zero-shot任务迁移学习
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20190221 arXiv Adaptive Cross-Modal Few-Shot Learning
- Adaptive cross-modal few-shot learning
- 跨模态的few-shot
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20180612 CVPR-18 泛化的Zero-shot learning:Generalized Zero-Shot Learning via Synthesized Examples
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20181106 arXiv Zero-Shot Transfer VQA Dataset
- English: A dataset for zero-shot VQA transfer
- 中文:一个针对zero-shot VQA的迁移学习数据集
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20171222 NIPS 2017 用adversarial网络,当target中有很少量的label时如何进行domain adaptation:Few-Shot Adversarial Domain Adaptation
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20181225 arXiv Learning Compositional Representations for Few-Shot Recognition
- Few-shot recognition
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20181127 WACV-19 Self Paced Adversarial Training for Multimodal Few-shot Learning
- Multimodal training for single modal testing
- 用多模态数据针对单一模态进行迁移
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20180728 arXiv Meta-learning autoencoders for few-shot prediction
- Using meta-learning for few-shot transfer learning
- 用元学习进行迁移学习
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20171216 arXiv Zero-Shot Deep Domain Adaptation
- 当target domain的数据不可用时,如何用相关domain的数据进行辅助学习?
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20191204 arXiv MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
- Task adaptive structure for few-shot learning
- 目标自适应的结构用于小样本学习
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20190409 ICLR-19 A Closer Look at Few-shot Classification
- Give some important conclusions on few-shot classification
- 在few-shot上给了一些有用的结论
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20190401 IJCNN-19 Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration
- Zero-shot image recognition
- 零次学习的图像识别
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20171022 ICCVW-17 Zero-shot learning posed as a missing data problem
- 算法首先学习 semantic embeddings 的结构性知识,利用学习到的知识和已知类的 image features 合成未知类的 image features。再利用无标记的未知类数据对合成数据进行修正。 算法假设未知类数据呈混合高斯分布,用 GMM-EM 算法进行无监督修正。
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20180516 arXiv-18 A Large-scale Attribute Dataset for Zero-shot Learning
- 传统 ZSL 数据集(如 AwA, CUB)存在规模小,属性标注不丰富等问题。本文提出一个新的属性数据集 LAD 用于测试零样本学习算法。新数据集包含 230 类, 78,017 张图片,标注了 359 种属性。基于此数据集举办了 AI Challenger 零样本学习竞赛。 110+ 支来自海内外的参赛队伍提交了成绩。
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20180710 ICML-18 MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
- 针对 L1 (欠拟合) 和 L2 (无特征选择、有偏) 正则项存在的问题,提出 MSplit LBI 用于同时实现特征选择和密集估计。在 Few-shot Learning 和 Zero-shot Learning 两个问题上进行了实验。实验表明 MSplit LBI 由优于 L1 和 L2。针对 ZSL 进行了特征可视化实验。
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20190108 WACV-19 Zero-shot Learning via Recurrent Knowledge Transfer
- 基于样本合成的零样本学习算法通常将 semantic embeddings 的知识迁移到 image features 以实现 ZSL。然而,这种 training 和 testing space 的不一致,会导致这种迁移失效。因此,本文提出 Space Shift Problem,并针对此问题,提出一种(在 image feature space 和 semantic embedding space 之间)递归传递知识的解决方案。
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Gap Minimization for Knowledge Sharing and Transfer
- Multitask learning with gap minimization
- 用于多任务学习的gap minimization方法
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20190806 KDD-19 Relation Extraction via Domain-aware Transfer Learning
- Relation extraction using transfer learning for knowledge base construction
- 利用迁移学习进行关系抽取
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20190531 arXiv Multi-task Learning in Deep Gaussian Processes with Multi-kernel Layers
- Multi-task learning in deep Gaussian process
- 深度高斯过程中的多任务学习
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20200927 Knowledge Distillation for Multi-task Learning
- 针对多任务学习的知识蒸馏
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20200914 ECML-PKDD-20 Towards Interpretable Multi-Task Learning Using Bilevel Programming
- 用bilevel programming解释多任务学习
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20191202 arXiv AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
- Learning what to share for multi-task learning
- 对多任务学习如何share
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20191125 AAAI-20 Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
- Adaptive activation network for deep multi-task learning
- 自适应的激活网络用于深度多任务学习
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20191015 arXiv Gumbel-Matrix Routing for Flexible Multi-task Learning
- Effective method for flexible multi-task learning
- 一种很有效的方法用于多任务学习
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20190718 arXiv Task Selection Policies for Multitask Learning
- Task selection in multitask learning
- 在多任务学习中的任务选择机制
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20190509 FG-19 Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation
- Multi-task human analysis in still images
- 多任务人体静止图像分析
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20190409 NAACL-19 AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning
- Automatic Task Selection and Mixing in Multi-Task Learning
- 多任务学习中自动任务选择和混淆
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20190409 TNNLS-19 Heterogeneous Multi-task Metric Learning across Multiple Domains
- Heterogeneous Multi-task Metric Learning across Multiple Domains
- 在多个领域之间进行异构多任务度量学习
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20190409 NeurIPS-18 Synthesized Policies for Transfer and Adaptation across Tasks and Environments
- Transfer across tasks and environments
- 通过任务和环境之间进行迁移
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20190408 ICMR-19 Learning Task Relatedness in Multi-Task Learning for Images in Context
- Using task relatedness in multi-task learning
- 在多任务学习中学习任务之间的相关性
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20190408 CVPR-19 End-to-End Multi-Task Learning with Attention
- End-to-End Multi-Task Learning with Attention
- 基于attention的端到端的多任务学习
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20190401 arXiv Many Task Learning with Task Routing
- From multi-task leanring to many-task learning
- 许多任务同时学习
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20190324 arXiv A Principled Approach for Learning Task Similarity in Multitask Learning
- Provide some theoretical analysis of the similarity learning in multi-task learning
- 为多任务学习中的相似度学习提供了一些理论分析
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20181128 arXiv A Framework of Transfer Learning in Object Detection for Embedded Systems
- A Framework of Transfer Learning in Object Detection for Embedded Systems
- 一个用于嵌入式系统的迁移学习框架
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20181012 NIPS-18 Multi-Task Learning as Multi-Objective Optimization
- Solve the multi-task learning as a multi-objective optimization problem
- 将多任务问题看成一个多目标优化问题进行求解
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20181008 PSB-19 The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
- Evaluate the effectiveness of multitask learning for phenotyping
- 评估多任务学习对于表型的作用
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20180828 arXiv Self-Paced Multi-Task Clustering
- Multi-task clustering
- 多任务聚类
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20180622 arXiv 探索了多任务迁移学习中的不确定性:Uncertainty in Multitask Transfer Learning
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20180524 arXiv 杨强团队、与之前的learning to learning类似,这里提供了一个从经验中学习的learning to multitask框架:Learning to Multitask
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Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems
- Multi-agent transfer in RL
- 在RL中的多智能体迁移
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NeurIPS-21 workshop Component Transfer Learning for Deep RL Based on Abstract Representations
- Deep transfer learning for RL
- 深度迁移学习用于强化学习
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Xi-Learning: Successor Feature Transfer Learning for General Reward Functions
- General reward function transfer learning in RL
- 在强化学习中general reward function的迁移学习
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NeurIPS-21 Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
- Domain adaptation in reinforcement learning
- 在强化学习中应用domain adaptation
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Understanding Domain Randomization for Sim-to-real Transfer
- Understanding domain randomizationfor sim-to-real transfer
- 对强化学习中的sim-to-real transfer进行理论上的分析
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20191214 arXiv Does Knowledge Transfer Always Help to Learn a Better Policy?
- Transfer learning in reinforcement learning
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20191212 AAAI-20 Transfer value iteration networks
- Transferred value iteration networks
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20190821 arXiv Transfer in Deep Reinforcement Learning using Knowledge Graphs
- Use knowledge graph to transfer in reinforcement learning
- 用知识图谱进行强化迁移
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20190320 arXiv Learning to Augment Synthetic Images for Sim2Real Policy Transfer
- Augment synthetic images for sim to real policy transfer
- 学习对于策略迁移如何合成图像
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20190305 arXiv [Sim-to-Real Transfer for Biped Locomotion]
- Transfer learning for robot locomotion
- 用迁移学习进行机器人定位
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20190220 arXiv DIViS: Domain Invariant Visual Servoing for Collision-Free Goal Reaching
- Transfer learning for robot reinforcement learning
- 迁移学习用于机器人的强化学习目标搜寻
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20181212 NeurIPS-18 workshop Efficient transfer learning and online adaptation with latent variable models for continuous control
- Reinforcement transfer learning with latent models
- 隐变量模型用于迁移强化学习的控制
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20181128 arXiv Hardware Conditioned Policies for Multi-Robot Transfer Learning
- Hardware Conditioned Policies for Multi-Robot Transfer Learning
- 多个机器人之间的迁移学习
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20180926 arXiv Target Transfer Q-Learning and Its Convergence Analysis
- Analyze the risk of transfer q-learning
- 提供了在�Q learning的任务迁移中一些理论分析
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20180926 arXiv Domain Adaptation in Robot Fault Diagnostic Systems
- Apply domain adaptation in robot fault diagnostic system
- 将domain adaptation应用于机器人故障检测系统
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20180912 arXiv VPE: Variational Policy Embedding for Transfer Reinforcement Learning
- Policy transfer in reinforcement learning
- 增强学习中的策略迁移
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20180909 arXiv Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation
- deep + adversarial + reinforcement learning transfer
- 深度对抗迁移学习用于强化学习
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20180530 ICML-18 强化迁移学习:Importance Weighted Transfer of Samples in Reinforcement Learning
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20180524 arXiv 用深度强化学习的方法学习domain adaptation中的采样策略:Learning Sampling Policies for Domain Adaptation
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20180516 arXiv 探索了强化学习中的任务迁移:Adversarial Task Transfer from Preference
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20180413 NIPS-17 基于后继特征迁移的强化学习:Successor Features for Transfer in Reinforcement Learning
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20180404 IEEE TETCI-18 用迁移学习来玩星际争霸游戏:StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
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20190515 TNNLS-19 A Distributed Approach towards Discriminative Distance Metric Learning
- Discriminative distance metric learning
- 分布式度量学习
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20190409 TNNLS-19 Heterogeneous Multi-task Metric Learning across Multiple Domains
- Heterogeneous Multi-task Metric Learning across Multiple Domains
- 在多个领域之间进行异构多任务度量学习
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20190409 PAMI-19 Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain
- Heterogeneous transfer metric learning by transferring fragments
- 通过迁移知识片段来进行异构迁移度量学习
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20190409 arXiv Decomposition-Based Transfer Distance Metric Learning for Image Classification
- Transfer metric learning based on decomposition
- 基于特征向量分解的迁移度量学习
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20181012 arXiv Transfer Metric Learning: Algorithms, Applications and Outlooks
- A survey on transfer metric learning
- 一篇迁移度量学习的综述
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20180622 arXiv 基于深度迁移学习的度量学习:DEFRAG: Deep Euclidean Feature Representations through Adaptation on the Grassmann Manifold
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20181117 arXiv Distance Measure Machines
- Machines that measures distances
- 衡量距离的算法
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20180605 KDD-10 迁移度量学习:Transfer metric learning by learning task relationships
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20180606 arXiv 将流形和统计信息联合起来构成一个domain adaptation框架:A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
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20180605 CVPR-15 深度度量迁移学习:Deep metric transfer learning
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ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning [arxiv]
- Black-box foundation models for personalized federated learning 黑盒的blackbox模型进行个性化迁移学习
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Benchmarking Algorithms for Federated Domain Generalization [arxiv]
- Benchmark algorthms for federated domain generalization 对联邦域泛化算法进行的benchmark
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IEEE'23 FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning [arxiv]
- Fast generalization for federated CLIP 在联邦中进行快速的CLIP训练
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Federated Semi-Supervised Domain Adaptation via Knowledge Transfer
- Federated semi-supervised DA 联邦半监督DA
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FL-IJCAI-22 MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
- MetaFed: a new form of federated learning
- 联邦之联邦学习、新范式
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Interspeech-22 Decoupled Federated Learning for ASR with Non-IID Data
- Decoupled federated learning for non IID
- 解耦的联邦架构用于Non-IID语音识别
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Test-Time Robust Personalization for Federated Learning
- Test-time robust personalization for FL
- 测试时鲁棒联邦学习
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IEEE TNNLS-22 Towards Personalized Federated Learning
- A survey on personalized federated learning
- 一个关于个性化联邦学习的综述
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Improving Generalization in Federated Learning by Seeking Flat Minima
- Seeking flat minima for domain generalization in federated learning
- 通过寻找平坦值进行联邦学习领域泛化
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SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
- Personalized federated learning
- 个性化联邦学习
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NeurIPS-21 Parameterized Knowledge Transfer for Personalized Federated Learning
- personalized group knowledge transfer training
- 个性化群体知识迁移
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ICML-21 Federated Continual Learning with Weighted Inter-client Transfer
- Federated Weighted Inter-client Transfer (FedWeIT) for Federated Continual Learning
- 联邦加权客户端间传输方法,用于联邦持续学习
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SIGIR-21 FedCT: Federated Collaborative Transfer for Recommendation
- Federated learning for cross-domain recommendation
- 使用联邦迁移学习执行跨域推荐任务
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KDD-21 Federated Adversarial Debiasing for Fair and Transferable Representations
- Federated Adversarial DEbiasing (FADE)
- 通过对抗性学习对联邦学习过程去除偏见
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Federated Learning with Adaptive Batchnorm for Personalized Healthcare
- Federated learning with adaptive batchnorm
- 用自适应BN进行个性化联邦学习
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FedZKT: Zero-Shot Knowledge Transfer towards Heterogeneous On-Device Models in Federated Learning
- Zero-shot transfer in heterogeneous federated learning
- 零次迁移用于联邦学习
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Federated Multi-Task Learning under a Mixture of Distributions
- Federated multi-task learning
- 联邦多任务学习
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NeurIPS-20 Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
- Group knowledge transfer training
- 群体知识迁移
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Fine-tuning is Fine in Federated Learning
- Finetuning in federated learning
- 在联邦学习中进行finetune
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Federated Multi-Target Domain Adaptation
- Federated multi-target DA
- 联邦学习场景下的多目标DA
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20190909 IJCAI-FML-19 FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
- The first work on federated transfer learning for wearable healthcare
- 第一个将联邦迁移学习用于可穿戴健康监护的工作
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20180605 arXiv 解决federated learning中的数据不同分布的问题:Federated Learning with Non-IID Data
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20190301 NeurIPS-18 workshp One-Shot Federated Learning
- One-shot federated learning
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Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]
- Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习
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TMLR'23 Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks [arxiv]
- A framework for online learning 一个在线学习的框架
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NeurIPS'22 Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer [arxiv]
- Continual learning with backward knowledge transfer 反向知识迁移的持续学习
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Mixture of basis for interpretable continual learning with distribution shifts
- Incremental learning with mixture of basis
- 用mixture of domains进行增量学习
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20101008 arXiv Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation
- Continuous adaptation for time series data
- 对时间序列进行连续adaptation
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20191011 arXiv Learning to Remember from a Multi-Task Teacher
- Dealing with the catastrophic forgetting during sequential learning
- 在序列学习时处理灾难遗忘
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20191029 Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning
- Avoid catastrophic forgeeting in incremental task lifelong learning
- 在终身学习中避免灾难遗忘
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20200706 [ICML-20] Continuously Indexed Domain Adaptation
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20210716 TPAMI-21 Lifelong Teacher-Student Network Learning
- Lifelong distillation
- 持续的知识蒸馏
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20210716 ICML-21 Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
- Investigating task similarity in teacher-student learning
- 调研在continual learning下teacher-student learning问题的任务相似度
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20190912 NeurIPS-19 Meta-Learning with Implicit Gradients
- Meta-learning with implicit gradients
- 隐式梯度的元学习
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20180323 arXiv 终身迁移学习与增量学习结合:Incremental Learning-to-Learn with Statistical Guarantees
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20180111 arXiv 一种新的终身学习框架,与L2T的思路有一些类似 Lifelong Learning for Sentiment Classification
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ICSE-22 ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing | Code | Blog | Video
- Safe transfer learning by reducing defect inheritance
- 安全迁移学习的最新工作
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CVPR workshop-21 Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness
- Improve adversarial robustness of transfer learning models
- 提高迁移学习对于adversarial robustness的鲁棒性
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ICLR-20 A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
- Softmax layer is easy to get attacked
- 设计实验来攻击迁移学习的softmax layer
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RAID'18 Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
- Finetune and prune the weights against backdoor attack
- 在finetune过程中剪枝来预防后门攻击
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ACM CCS-18 Model-Reuse Attacks on Deep Learning Systems
- Model-resuse attack on transfer learning models
- 设计实验来攻击迁移学习的预训练模型
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USENIX Security-18 With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning
- First work to design experiments to attack pretrained models
- 第一个设计实验来攻击预训练模型的工作
See HERE for a full list of transfer learning applications.