NeurAI-Lab / SYNERgy

The official PyTorch code for CoLLAs 2022 paper "SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning"

SYNERgy

Official Repository for CoLLAs 2022 paper "SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning".

We extended the CLS-ER repo with our method (SYNERgy).

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 for SYNERgy in the paper run the following python main.py --dataset <dataset> --model synergy --buffer_size <buffer_size> --load_best_args

Examples:

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

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

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

python main.py --dataset gcil-cifar100 --weight_dist unif --model synergy --load_best_args

python main.py --dataset gcil-cifar100 --weight_dist longtail --model synergy --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{pmlr-v199-sarfraz22a,
   title = {SYNERgy between SYNaptic Consolidation and Experience Replay for General Continual Learning},
   author = {Sarfraz, Fahad and Arani, Elahe and Zonooz, Bahram},
   booktitle = {Proceedings of The 1st Conference on Lifelong Learning Agents},
   pages = {920--936},
   year = {2022},
   editor = {Chandar, Sarath and Pascanu, Razvan and Precup, Doina},
   volume = {199},
   series = {Proceedings of Machine Learning Research},
   month = {22--24 Aug},
   publisher = {PMLR},
   pdf = {https://proceedings.mlr.press/v199/sarfraz22a/sarfraz22a.pdf},
   url = {https://proceedings.mlr.press/v199/sarfraz22a.html},
}