New Insights for the Stability-Plasticity Dilemma in Online Continual Learning (MuFAN)

This project contains the implementation of the following ICLR 2023 paper:

Title: New Insights for the Stability-Plasticity Dilemma in Online Continual Learning (ICLR 2023) [openreview].

Authors: Dahuin Jung, Dongjin Lee, Sunwon Hong, Hyemi Jang, Ho Bae, Sungroh Yoon

MuFAN proposes a novel online continual learning framework that utilizes multi-scale feature maps in addition to a structure-wise distillation loss and a stability-plasticity normalization module to maintain high stability and plasticity simultaneously.

Requirements

• python 3.8.13
• pytorch 1.11.0
• torchvision 0.8.1
• timm 0.4.9

Benchmarks

1. Prepare data

The data/ folders contains the train and test splits for the miniImageNet and CORE50 benchmarks. Download the raw data and modify the path in the csv files to point to the raw data folder.

2. Run experiments

chmod +x scripts/task_aware.sh
bash scripts/task_aware.sh 0

The results will be put in the resuts/ folders.

Acknowledgement

This project structure is based on the DualNet repository.

• https://github.com/phquang/DualNet

Citation

If you found MuFAN useful for your research, please consider citing.

@inproceedings{
jung2023new,
title={New Insights for the Stability-Plasticity Dilemma in Online Continual Learning},
author={Dahuin Jung and Dongjin Lee and Sunwon Hong and Hyemi Jang and Ho Bae and Sungroh Yoon},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=fxC7kJYwA_a}
}