We propose an effective regularization strategy (CW-TaLaR) for solving continual learning problems. It uses a penalizing term expressed by the Cramer-Wold distance between two probability distributions defined on a target layer of an underlying neural network that is shared by all tasks, and the simple architecture of the Cramer-Wold generator for modeling output data representation. Our strategy preserves target layer distribution while learning a new task but does not require remembering previous tasks' datasets. We perform experiments involving several common supervised frameworks, which prove the competitiveness of the CW-TaLaR method in comparison to a few existing state-of-the-art continual learning models.
@article{arxiv.2111.07928,
title = {Target Layer Regularization for Continual Learning Using Cramer-Wold Generator},
author = {Marcin Mazur and Łukasz Pustelnik and Szymon Knop and Patryk Pagacz and Przemysław Spurek},
journal= {arXiv preprint arXiv:2111.07928},
year = {2021}
}
Comments
The paper is under consideration at Computer Vision and Image Understanding