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Learning Fast, Learning Slow

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

Screenshot 2023-08-07 at 10 47 11

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}
}