Computer Science > Machine Learning
[Submitted on 6 Dec 2019 (v1), last revised 4 Apr 2021 (this version, v4)]
Title:Regularization Shortcomings for Continual Learning
View PDFAbstract:In most machine learning algorithms, training data is assumed to be independent and identically distributed (iid). When it is not the case, the algorithm's performances are challenged, leading to the famous phenomenon of catastrophic forgetting. Algorithms dealing with it are gathered in the Continual Learning research field. In this paper, we study the regularization based approaches to continual learning and show that those approaches can not learn to discriminate classes from different tasks in an elemental continual benchmark: the class-incremental scenario. We make theoretical reasoning to prove this shortcoming and illustrate it with examples and experiments. Moreover, we show that it can have some important consequences on continual multi-tasks reinforcement learning or in pre-trained models used for continual learning. We believe that highlighting and understanding the shortcomings of regularization strategies will help us to use them more efficiently.
Submission history
From: Timothée Lesort [view email][v1] Fri, 6 Dec 2019 10:11:18 UTC (395 KB)
[v2] Fri, 7 Feb 2020 12:10:55 UTC (443 KB)
[v3] Tue, 8 Dec 2020 17:25:56 UTC (498 KB)
[v4] Sun, 4 Apr 2021 00:21:23 UTC (754 KB)
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