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 novel collaborative learning scheme to boost the training of the models. We adapted 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.

翻译：在线持续学习（Online CL）旨在解决从连续数据流中不断学习新分类任务的问题。与离线持续学习不同，在线持续学习中训练数据仅能被观测一次。现有大多数在线持续学习研究将灾难性遗忘（即模型稳定性）视为几乎唯一的挑战。本文提出，模型获取新知识的能力（即模型可塑性）是在线持续学习中的另一项挑战。虽然基于回放的策略已被证明能有效缓解灾难性遗忘，但在提升模型可塑性方面的研究关注度存在显著缺口。为此，我们提出协作持续学习（CCL）——一种基于协作学习的策略，用于提升模型获取新概念的能力。此外，我们引入蒸馏链（DC）这一新型协作学习方案，以加速模型训练。我们将CCL-DC适配至现有代表性在线持续学习方法中。大量实验表明，即使学习者已通过最先进的在线持续学习方法充分训练，我们的策略仍能显著提升模型可塑性，从而大幅提升整体性能。