This is a PyTorch implementation of the continual learning experiments with deep neural networks described in the following article:

• Looking through the past: better knowledge retention for generative replay in continual learning (under review) https://arxiv.org/abs/2309.10012

Short version of this work is presented on Continual Learning worskshop at ICCV2023.

This repository is based on continual learning replay: https://github.com/GMvandeVen/continual-learning

Experiments are performed in the academic continual learning setting, whereby a classification-based problem is split up into multiple, non-overlapping contexts (or tasks, as they are often called) that must be learned sequentially.

The current version of the code has been tested with Python 3.10.4 on a Fedora operating system with the following versions of PyTorch and Torchvision:

• pytorch 1.11.0
• torchvision 0.12.0

Further Python-packages used are listed in requirements.txt. Assuming Python and pip are set up, these packages can be installed using:

pip install -r requirements.txt

The code in this repository itself does not need to be installed, but a number of scripts should be made executable:

chmod +x exps.sh exps_cycles.sh

./exps.sh

This runs experiments for BIR, BIR+SI and other methods for comparison for seed=1. Run this command on develop branch.

./exps_cycles.sh

This runs experiments for our method for seed=1. Run this command on laten_match_dist_cycle branch.

Please consider citing our papers if you use this code in your research:

@article{khan2023looking,
  title={Looking through the past: better knowledge retention for generative replay in continual learning},
  author={Khan, Valeriya and Cygert, Sebastian and Deja, Kamil and Trzci{\'n}ski, Tomasz and Twardowski, Bart{\l}omiej},
  journal={arXiv preprint arXiv:2309.10012},
  year={2023}
}