Repeated Augmented Rehearsal (RAR) for online continual learning

This is the official code repository for Repeated Augmented Rehearsal (NeurIPS 2022). If you use any content of this repo for your work, please cite the following bib entry:

Citation

@inproceedings{NEURIPS2022_5ebbbac6,
 author = {Zhang, Yaqian and Pfahringer, Bernhard and Frank, Eibe and Bifet, Albert and Lim, Nick Jin Sean and Jia, Yunzhe},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {14771--14783},
 title = {A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/5ebbbac62b968254093023f1c95015d3-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

Requirements

Create a virtual enviroment

virtualenv rar

Activating a virtual environment

source rar/bin/activate

Installing packages

pip install -r requirements.txt

Run commands

bash runs

A test run of Repeated Augmented Rehearsal(RAR) with experience replay can be performed with the following command:

bash run_commands/runs/run_test_rar_er_cifar100.sh

Other experiment commands can be found in the folder of run_commands/runs.

Detailed descriptions of options can be found in general_main.py and utils/argparser

For example:

The number of repeated iteration is set via:

--mem_iters $MEM_ITER

The number of augmentation strength is set via:

--randaug True --randaug_N $RAUG_N  --randaug_M $RAUG_M

Evaluation the results

The results of algorithm outputs will be stored in the folder of results.

The jupyter notebook visualize_results.ipynb is used to visualize and analyze results.

Algorithms

Baselines

LwF: Learning without forgetting (ECCV, 2016) [Paper]
AGEM: Averaged Gradient Episodic Memory (ICLR, 2019) [Paper]
ER: Experience Replay (ICML Workshop, 2019) [Paper]
ASER: Adversarial Shapley Value Experience Replay(AAAI, 2021) [Paper]
MIR: Maximally Interfered Retrieval (NeurIPS, 2019) [Paper]
SCR: Supervised Contrastive Replay (CVPR Workshop, 2021) [Paper]
DER: Dark Experience Replay (NeurIPS, 2020) [Paper]

Datasets

Online Class Incremental

Split CIFAR100
Split Mini-ImageNet
CORe50-NC
CLRS-NC (Continual Learning Benchmark for Remote Sensing Image Scene Classification)

Data preparation

CIFAR100 will be downloaded during the first run
CORE50 download: source fetch_data_setup.sh
Mini-ImageNet: Download from https://www.kaggle.com/whitemoon/miniimagenet/download , and place it in datasets/mini_imagenet/
CLRS: Download from https://github.com/lehaifeng/CLRS

Acknowledgments

Thanks for the great code base from:

HqChen2021/RepeatedAugmentedRehearsal