Learning Equi-angular Representations for Online Continual Learning

• URL: http://arxiv.org/abs/2404.01628v1
• Date: Tue, 2 Apr 2024 04:29:01 GMT
• Title: Learning Equi-angular Representations for Online Continual Learning
• Authors: Minhyuk Seo, Hyunseo Koh, Wonje Jeung, Minjae Lee, San Kim, Hankook Lee, Sungjun Cho, Sungik Choi, Hyunwoo Kim, Jonghyun Choi,
• Abstract summary: In particular, we induce neural collapse to form a simplex equiangular tight frame (ETF) structure in the representation space. We show that our proposed method outperforms state-of-the-art methods by a noticeable margin in various online continual learning scenarios.
• Score: 28.047867978274358
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training). To address the challenge, we propose an efficient online continual learning method using the neural collapse phenomenon. In particular, we induce neural collapse to form a simplex equiangular tight frame (ETF) structure in the representation space so that the continuously learned model with a single epoch can better fit to the streamed data by proposing preparatory data training and residual correction in the representation space. With an extensive set of empirical validations using CIFAR-10/100, TinyImageNet, ImageNet-200, and ImageNet-1K, we show that our proposed method outperforms state-of-the-art methods by a noticeable margin in various online continual learning scenarios such as disjoint and Gaussian scheduled continuous (i.e., boundary-free) data setups.

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