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}
}