Target Layer Regularization for Continual Learning Using Cramer-Wold Generator

Marcin Mazur; Łukasz Pustelnik; Szymon Knop; Patryk Pagacz; Przemysław Spurek

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arXiv · 2111.07928

Target Layer Regularization for Continual Learning Using Cramer-Wold Generator

Marcin Mazur·Łukasz Pustelnik·Szymon Knop·Patryk Pagacz·Przemysław Spurek

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Abstract

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.

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Marcin Mazur, Łukasz Pustelnik, Szymon Knop, Patryk Pagacz, Przemysław Spurek. 2021-11-15. Target Layer Regularization for Continual Learning Using Cramer-Wold Generator. https://arxiv.org/abs/2111.07928

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