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☕ (MOCA) Continual Learning by Modeling Intra-Class Variation

This is an official implementation of the TMLR 2023 paper "Continual Learning by Modeling Intra-Class Variation" (MOCA).

Environment

This work is based on the code of DER:

pip install -r requirements.txt

Setup

• Use ./utils/main.py to run experiments.
• Use argument --load_best_args to use the best hyperparameters from the paper.

TODO:

• Release code!
• Bash Arguments!
• The code is still dirty and we'll sort it out soon.
• 2D Visualization code and Gradients Analysis code.

Examples

For reproducing the results of our MOCA-Gaussian on Cifar-100, run:

python ./utils/main.py --load_best_args --model er --dataset seq-cifar100 --buffer_size 500  --para_scale 1.5 --gamma_loss 1 --norm_add norm_add --method2 gaussian --noise_type noise

For reproducing the results of our MOCA-WAP on Cifar-100, run:

python ./utils/main.py --load_best_args --model er --dataset seq-cifar100 --buffer_size 500  --para_scale 1.5 --gamma_loss 10  --norm_add norm_add --advloss none --target_type new_labels --noise_type adv --inner_iter 1

Citation

If you find this code or idea useful, please cite our work:

@article{yu2022continual,
  title={Continual Learning by Modeling Intra-Class Variation},
  author={Yu, Longhui and Hu, Tianyang and Hong, Lanqing and Liu, Zhen and Weller, Adrian and Liu, Weiyang},
  journal={arXiv preprint arXiv:2210.05398},
  year={2022}
}

Contact

If you have any questions, feel free to contact us through email (yulonghui@stu.pku.edu.cn). Enjoy!

Intra-class Variation Gap

Representation Collapse

Gradient Collapse

MOCA Framework

MOCA Variants