Awesome

ESMER

Official Repository for ICLR'23 Paper "Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning"

We extended the CLS-ER repo with our method

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-cifar10 --model esmer --buffer_size 200 --load_best_args
  
  python main.py --dataset seq-cifar100 --model esmer --buffer_size 200 --load_best_args
  

  For GCIL-CIFAR-100 Experiments

  python main.py --dataset gcil-cifar100 --weight_dist unif --model esmer --buffer_size 200 --load_best_args
  
  python main.py --dataset gcil-cifar100 --weight_dist longtail --model esmer --buffer_size 200 --load_best_args
  

Requirements

• torch==1.7.0
• torchvision==0.9.0

Cite Our Work

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

@inproceedings{
  sarfrazerror,
  title={Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning},
  author={Sarfraz, Fahad and Arani, Elahe and Zonooz, Bahram},
  booktitle={The Eleventh International Conference on Learning Representations},
  year={2023},
  url={https://openreview.net/forum?id=zlbci7019Z3}
}