FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental Learning

Official implementation of "[FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental Learning](https://openaccess.thecvf.com/content/CVPR2024/html/Li_FCS_Feature_Calibration_and_Separation_for_Non-Exemplar_Class_Incremental_Learning_CVPR_2024_paper.html)"

Requirements

Environment

Python 3.7.13

PyTorch 1.8.1

Run commands

Json files for different experinments are provided in ./exps/fcs/

Run algorithms on CIFAR100-5stages

python main.py --config=./exps/fcs/cifar100/5/first_stage.json # base stage
python main.py --config=./exps/fcs/cifar100/5/second_stage.json # incremental learning

Results

Results for different experinments are provided in ./files/results.txt

Acknowledgement

This project is mainly based on PyCIL.

Citation

If you find this work helpful, please cite:

@inproceedings{li2024fcs,
  title={FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental Learning},
  author={Li, Qiwei and Peng, Yuxin and Zhou, Jiahuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={28495--28504},
  year={2024}
}

Contact

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