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Divide and not forget: Ensemble of selectively trained experts in Continual Learning: ICLR2024 (Main track)

https://arxiv.org/abs/2401.10191
https://openreview.net/forum?id=sSyytcewxe

This repository contains code for the SEED paper published at the main track of ICLR2024. It is based on FACIL (https://github.com/mmasana/FACIL) benchmark. To reproduce results run one of provided scripts.

Setup environment according to readme of FACIL.

Run SEED on CIFAR100 10 tasks, 10 classes each:

bash cifar10x10.sh

Run SEED on CIFAR100 20 tasks, 5 classes each:

bash cifar20x5.sh

Run SEED on CIFAR100 50 tasks, 2 classes each:

bash cifar50x2.sh

To lower the number of parameters as in Tab.5 use --network resnet 20 --shared 2. You can also add parameter pruning as in DER.

To reproduce results for ImageNet Subset download ImageNet subset from https://www.kaggle.com/datasets/arjunashok33/imagenet-subset-for-inc-learn and unzip it in ../data directory.

bash imagenet10x10.sh

To reproduce results for DomainNet download it from http://ai.bu.edu/M3SDA/ and put it in ../data/domainnet directory (unzip it). Run SEED on DomainNet 36 tasks of different domains, 5 classes each:

bash domainnet36x5.sh

You can add --extra-aug fetril flag to enable better augmentations.

If you would like to cooperate on improving the method, please contact me via LinkedIn or Facebook, I have several ideas.

If you find this work useful, please consider citing it:

@inproceedings{rypesc2023divide,
  title={Divide and not forget: Ensemble of selectively trained experts in Continual Learning},
  author={Rype{\'s}{\'c}, Grzegorz and Cygert, Sebastian and Khan, Valeriya and Trzcinski, Tomasz and Zieli{\'n}ski, Bartosz Micha{\l} and Twardowski, Bart{\l}omiej},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2023}
}