Improving Plasticity in Online Continual Learning via Collaborative Learning

• URL: http://arxiv.org/abs/2312.00600v2
• Date: Sun, 31 Mar 2024 12:45:09 GMT
• Title: Improving Plasticity in Online Continual Learning via Collaborative Learning
• Authors: Maorong Wang, Nicolas Michel, Ling Xiao, Toshihiko Yamasaki,
• Abstract summary: We argue that the model's capability to acquire new knowledge (i.e., model plasticity) is another challenge in online CL. We propose Collaborative Continual Learning (CCL), a collaborative learning based strategy to improve the model's capability in acquiring new concepts.
• Score: 22.60291297308379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online Continual Learning (CL) solves the problem of learning the ever-emerging new classification tasks from a continuous data stream. Unlike its offline counterpart, in online CL, the training data can only be seen once. Most existing online CL research regards catastrophic forgetting (i.e., model stability) as almost the only challenge. In this paper, we argue that the model's capability to acquire new knowledge (i.e., model plasticity) is another challenge in online CL. While replay-based strategies have been shown to be effective in alleviating catastrophic forgetting, there is a notable gap in research attention toward improving model plasticity. To this end, we propose Collaborative Continual Learning (CCL), a collaborative learning based strategy to improve the model's capability in acquiring new concepts. Additionally, we introduce Distillation Chain (DC), a collaborative learning scheme to boost the training of the models. We adapt CCL-DC to existing representative online CL works. Extensive experiments demonstrate that even if the learners are well-trained with state-of-the-art online CL methods, our strategy can still improve model plasticity dramatically, and thereby improve the overall performance by a large margin. The source code of our work is available at https://github.com/maorong-wang/CCL-DC.

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