This paper explores the connection between two recently identified phenomena in deep learning: plasticity loss and neural collapse. We analyze their correlation in different scenarios, revealing a significant association during the initial training phase on the first task. Additionally, we introduce a regularization approach to mitigate neural collapse, demonstrating its effectiveness in alleviating plasticity loss in this specific setting.
@article{arxiv.2404.02719,
title = {Can We Understand Plasticity Through Neural Collapse?},
author = {Guglielmo Bonifazi and Iason Chalas and Gian Hess and Jakub Łucki},
journal= {arXiv preprint arXiv:2404.02719},
year = {2024}
}