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<div align="center"><b> A Unified Continual Learning Framework with General <br /> Parameter-Efficient Tuning </b></div>

<div align="center">

Qiankun Gao, Chen Zhao, Yifan Sun, Teng Xi, Gang Zhang, Bernard Ghanem, Jian Zhang

[Paper] [Supp] [arXiv] [BibTex]

<br /> <img src='https://github.com/gqk/LAE/assets/73707470/4db6b2d1-7c2e-4211-a4e4-6bf8380d0132' width="80%" /> </div>

News

Installation

Dataset

  1. Create a dataset root diretory, e.g., data.

  2. CIFAR100 and ImageNet-R datasets will be automatically downloaded, while DomainNet requires manual download.

  3. Overview of dataset root diretory

    ├── cifar100
    │   └── cifar-100-python
    ├── domainnet
    │   ├── clipart
    │   ├── infograph
    │   ├── painting
    │   ├── quickdraw
    │   ├── real
    │   └── sketch
    └── imagenet-r
        ├── imagenet-r
        ├── train_list.txt
        └── val_list.txt
    

    :warning: The train-validation split of ImageNet-R dataset are consistent with the L2P JAX code, replace the train_list.txt and val_list.txt with train_list_coda-p.txt and val_list_coda-p.txt if you want to use the train-validation splitation of CODA-Prompt.

Experiment

Acknowledgement

Citation

@inproceedings{gao2023lae,
  title={A Unified Continual Learning Framework with General Parameter-Efficient Tuning}
  author={Gao, Qiankun and Zhao, Chen and Sun, Yifan and Xi, Teng and Zhang, Gang and Ghanem, Bernard and Zhang, Jian},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages={11483--11493},
  year={2023}
}