CVPR2021 Incremental Learning
This repository is for the paper "Distilling Causal Effect of Data in Class-Incremental Learning".
# Instructions 1. Dependencies - Python 3.6 (Anaconda3 Recommended) - Pytorch 0.4.0 - torchvision 0.2.1 - numpy 1.18.1 2. Getting Started - the data for CIFAR100 and ImageNet are put in `cifar100-class-incremental/data` and `imagenet-class-incremental/data`, or you can make soft links to the directories which include the corresponding data - make soft links for `utils_incremental` folder under `cifar100-class-incremental` and `imagenet-class-incremental` - make folders `logs`, `results` and `checkpoint` under `cifar100-class-incremental` and `imagenet-class-incremental` - see `cifar100-class-incremental/run.sh` for the experiments on CIFAR100 - see `imagenet-class-incremental/run.sh` for the experiments on ImageNet-Subset - see `imagenet-class-incremental/run_all.sh` for the experiments on ImageNet-Full # Citation Please cite the following paper if you find this useful in your research: ``` @InProceedings{Hu_20121_CVPR, author = {Hu, Xinting and Tang, Kaihua and Miao, Chunyan and Hua, Xian-Sheng and Zhang, Hanwang}, title = {Distilling Causal Effect of Data in Class-Incremental Learning}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021} } ```