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
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 |
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Use
python main.pyto run experiments. -
Use argument
--load_best_argsto 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
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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
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torch==1.7.0
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torchvision==0.9.0
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quadprog==0.1.7
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}
}
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