CLearning is a general continual learning framework.

1. Features

• Supported incremental learning (IL) scenarios:

  Task-IL, Domain-IL, and Class-IL

• Supported datasets:

  MNIST, PMNIST, RMNIST, FIVE, SVHN, CIFAR10, CIFAR100, SUPER, TinyImageNet, miniImageNet, DTD, ImageNet100, CUB, ImageNet1000

• Supported training modes:

  Single GPU, Distributed Parallel (DP)

• Support flexibly specify model:

  model specified in '*.yaml' > default model

2. Methods

The current repository implements the following continual learning methods:

• Baseline
  • FineTune
• Regularization
  • EWC: Overcoming catastrophic forgetting in neural networks
  • LwF: Learning without forgetting
  • GPM: Gradient projection memory for continual learning
• Replay
  • AFC: Class-incremental learning by knowledge distillation with adaptive feature consolidation
  • ANCL: Achieving a better stability-plasticity trade-off via auxiliary networks in continual learning
  • CSCCT: Class-incremental learning with cross-space clustering and controlled transfer
  • iCaRL: icarl: Incremental classifier and representation learning
  • LUCIR: Learning a unified classifier incrementally via rebalancing
  • MTD: Class Incremental Learning with Multi-Teacher Distillation
  • OPC: Optimizing mode connectivity for class incremental learning
  • PODNet: Podnet: Pooled outputs distillation for small-tasks incremental learning
  • SSIL: Ss-il: Separated softmax for incremental learning
• Structure
  • AANet: Adaptive aggregation networks for class-incremental learning

3. Environment

3.1. Code Environment

• python==3.9
• pytorch==1.11.0
• continuum==1.2.4
• ...

The required packages are listed in files 'config/requirements.txt' and 'config/env.yml'. We recommend using Anaconda to manage packages, such as:

conda create -n torch1.11.0 python=3.9
conda activate torch1.11.0
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
cd config
pip install -r requirements.txt

3.2. Dataset

Folder Structures

├── data
│   ├── cifar-100-python
│   ├── ImageNet
│   ├── ImageNet100

The CIFAR-100 dataset can be automatically downloaded by running an arbitrary script of CIFAR-100 experiments. The ImageNet dataset must be pre-downloaded. The ImageNet-100 dataset can be generated by the script 'tool/gen_imagenet100.py'.

4. Command

python main.py --cfg config/baseline/finetune/finetune_cifar100.yaml --device 0 --note test

5. Acknowledgements

We appreciate the following GitHub repos a lot for their valuable code base:

• https://github.com/arthurdouillard/incremental_learning.pytorch
• https://github.com/yaoyao-liu/POD-AANets

6. Citation

We hope that our research can help you and promote the development of continual learning. If you find this work useful, please consider citing the corresponding paper:

@inproceedings{wen2024class,
  title={Class Incremental Learning with Multi-Teacher Distillation},
  author={Wen, Haitao and Pan, Lili and Dai, Yu and Qiu, Heqian and Wang, Lanxiao and Wu, Qingbo and Li, Hongliang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={28443--28452},
  year={2024}
}

@InProceedings{pmlr-v202-wen23b,
  title = {Optimizing Mode Connectivity for Class Incremental Learning},
  author = {Wen, Haitao and Cheng, Haoyang and Qiu, Heqian and Wang, Lanxiao and Pan, Lili and Li, Hongliang},
  booktitle = {Proceedings of the 40th International Conference on Machine Learning},
  pages = {36940--36957},
  year = {2023},
  volume = {202},
  publisher = {PMLR}
}