NeurAI-Lab / CLS-ER

The official PyTorch code for ICLR'22 Paper "Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System""

Learning Fast, Learning Slow

Official Repository for ICLR'22 Paper "Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System"

We extended the Mammoth framework with our method (CLS-ER) and GCIL-CIFAR-100 dataset

Additional Results

For a more extensive evaluation of our our method and benchmarking, we evaluated CLS-ER on S-CIFAR100 with 5 Tasks and also provide the Task-IL results for all the settings. Note that similar to DER, Task-IL results merely use logit masking at inference.

	S-MNIST		S-CIFAR-10		S-CIFAR-100		S-TinyImg
Buffer Size	Class-IL	Task-IL	Class-IL	Task-IL	Class-IL	Task-IL	Class-IL	Task-IL
200	89.54±0.21	97.97±0.17	66.19±0.75	93.90±0.60	43.80±1.89	73.49±1.04	23.47±0.80	49.60±0.72
500	92.05±0.32	98.95±0.10	75.22±0.71	94.94±0.53	51.40±1.00	78.12±0.24	31.03±0.56	60.41±0.50
5120	95.73±0.11	99.40±0.04	86.78±0.17	97.08±0.09	65.77±0.49	84.46±0.45	46.74±0.31	75.81±0.35

Setup

• Use python main.py to run experiments.

• Use argument --load_best_args to use the best hyperparameters for each of the evaluation setting from the paper.

• To reproduce the results in the paper run the following

  python main.py --dataset <dataset> --model <model> --buffer_size <buffer_size> --load_best_args

Examples:

python main.py --dataset seq-mnist --model clser --buffer_size 500 --load_best_args

python main.py --dataset seq-cifar10 --model clser --buffer_size 500 --load_best_args

python main.py --dataset seq-tinyimg --model clser --buffer_size 500 --load_best_args

python main.py --dataset perm-mnist --model clser --buffer_size 500 --load_best_args

python main.py --dataset rot-mnist --model clser --buffer_size 500 --load_best_args

python main.py --dataset mnist-360 --model clser --buffer_size 500 --load_best_args

• For GCIL-CIFAR-100 Experiments

  python main.py --dataset <dataset> --weight_dist <weight_dist> --model <model> --buffer_size <buffer_size> --load_best_args

Example:

python main.py --dataset gcil-cifar100 --weight_dist unif --model clser --buffer_size 500 --load_best_args

python main.py --dataset gcil-cifar100 --weight_dist longtail --model clser --buffer_size 500 --load_best_args

Requirements

• torch==1.7.0

• torchvision==0.9.0

• quadprog==0.1.7

Cite Our Work

If you find the code useful in your research, please consider citing our paper:

@inproceedings{
  arani2022learning,
  title={Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System},
  author={Elahe Arani and Fahad Sarfraz and Bahram Zonooz},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=uxxFrDwrE7Y}
}