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Improving Plasticity in Online Continual Learning via Collaborative Learning

Official implementation of the paper "Improving Plasticity in Online Continual Learning via Collaborative Learning". This paper is accepted by CVPR2024.

1. Dataset

CIFAR-10/100

Torchvision should be able to handle the CIFAR-10/100 dataset automatically. If not, please download the dataset from here and put it in the data folder.

TinyImageNet

This codebase should be able to handle TinyImageNet dataset automatically and save them in the data folder. If not, please refer to this github gist.

ImageNet-100

Download the ImageNet dataset from here and follow this for ImageNet-100 dataset generation. Put the dataset in the imagenet100_data folder. Symbolic links are highly recommended.

2. Reproduce our results

We use the following hardware and software for our experiments:

• Hardware: NVIDIA Tesla A100 GPUs
• Software: Please refer to requirements.txt for the detailed package versions. Conda is highly recommended.

3. Training

Weight and bias is highly recommended to run the training, and some features in this codebase is only available with weight and bias. However, it is possible to excute the training without weight and bias.

Training with a configuration file

Training can be done by specifying the dataset path and params in a configuration file, for example:

python main.py --data-root ./data --config ./config/CVPR24/cifar10/ER,c10,m500.yaml

Although we have attached the best hyperparameter with our search in ./config/CVPR24, we highly suggest to use weight and bias for the training.

Training with weight and bias sweep (Recommended)

Weight and bias sweep is originally designed for hyperparameter search. However, it make the multiple runs much easier. Training can be done with W&B sweep more elegantly, for example:

wandb sweep sweeps/CVPR/ER,cifar10.yaml

Note that you need to set the dataset path in .yaml file by specify --data-root-dir. And run the sweep agent with:

wandb agent $sweepID

The hyperparameters after our hyperparameter search is located at ./sweeps/CVPR.

4. Model / memory buffer snapshots

We save the model and memory buffer status after training for evaluation. After the training process, the model should be saved at ./checkpoints/$dataset/$learner/$memory_size/$rand_seed/model.pth and memory buffer should be located at ./checkpoints/$dataset/$learner/$memory_size/$rand_seed/memory.pkl.

5. Cite

@InProceedings{Wang_2024_CVPR,
    author    = {Wang, Maorong and Michel, Nicolas and Xiao, Ling and Yamasaki, Toshihiko},
    title     = {Improving Plasticity in Online Continual Learning via Collaborative Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {23460-23469}
}

Acknowledgement

Core contributors include Maorong and Nicolas. Special thanks to co-author Nicolas. Our implementation is based on his work AGD-FD's codebase.