The key training and evaluation codes for the AAAI-2024 paper "eTag: Class-Incremental Learning via Hierarchical Embedding Distillation and Task-Oriented Generation" exist.
- python = 3.8.5
- torch = 1.7.1
- torchvision = 0.8.2
Please confirm the dataset is well downloaded or softly linked in the file "./dataset" at first.
You can test our method by executing the script we provide, or by running the following command in the path "./scripts"
# on cifar with 5 tasks
bash -i run.sh cifar 0 5
# on cifar with 10 tasks
bash -i run.sh cifar 0 10
# on cifar with 25 tasks
bash -i run.sh cifar 0 25# on imagenet_sub with 5 tasks
bash -i run.sh imagenet 0 5
# on imagenet_sub with 10 tasks
bash -i run.sh imagenet 0 10
# on imagenet_sub with 25 tasks
bash -i run.sh imagenet 0 25-data: The name of the data you want to test, you can choose from one of them {cifar100,imagenet_sub} currently.-log_dir: Where the models, logs, and events are saved.-num_task: Number of tasks after initializing the first task.-nb_cl_fg: Number of classes in the first task.
For more tunable arguments, please look at the opts_eTag.py file.
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