Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning

Zhi-Hong Qi  Da-Wei Zhou  Yiran Yao  Han-Jia Ye  De-Chuan Zhan

School of Artificial Intelligence, State Key Laboratory for Novel Software Technology, Nanjing University 

The code repository for "Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning" (MJL 2024) in PyTorch.

🔧 Requirements

Environment

1. torch 1.11.0
2. torchvision 0.12.0
3. timm 0.6.12

Dataset

We provide the processed datasets as follows:

• CIFAR100: will be automatically downloaded by the code.

• ImageNet-R: Google Drive: link or Onedrive: link

• ObjectNet: Onedrive: link You can also refer to the filelist and processing code if the file is too large to download.

These subsets are sampled from the original datasets. Please note that I do not have the right to distribute these datasets. If the distribution violates the license, I shall provide the filenames instead.

You need to modify the path of the datasets in ./utils/data.py according to your own path.

💡 Running scripts

To prepare your JSON files, refer to the settings in the exps folder and run the following command. The results can be found in the logs folder.

python main.py --config ./exps/[configname].json

🎈 Acknowledgement

This repo is based on LAMDA-PILOT, CIL_Survey and PyCIL.

💭 Correspondence

If you have any questions, please contact me via email or open an issue.