Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams. Due to the time-varying training setting, the model learned from a changing distribution easily forgets the previously learned knowledge and biases toward the newly received task. To address this problem, we propose a Continual Bias Adaptor (CBA) module to augment the classifier network to adapt to catastrophic distribution change during training, such that the classifier network is able to learn a stable consolidation of previously learned tasks. In the testing stage, CBA can be removed which introduces no additional computation cost and memory overhead. We theoretically reveal the reason why the proposed method can effectively alleviate catastrophic distribution shifts, and empirically demonstrate its effectiveness through extensive experiments based on four rehearsal-based baselines and three public continual learning benchmarks.

翻译：在线持续学习旨在从非平稳数据流中学习新知识并巩固先前习得的知识。由于训练环境随时间变化，从变化分布中学习到的模型容易遗忘先前学到的知识，并对新接收的任务产生偏差。为解决这一问题，我们提出了一种持续偏差适配器模块，用于增强分类器网络以适应训练过程中发生的灾难性分布变化，从而使分类器网络能够稳定巩固先前学习的任务。在测试阶段，CBA可被移除，且不引入额外计算成本与内存开销。我们从理论上揭示了所提方法能有效缓解灾难性分布偏移的原因，并通过基于四个基于重放的基线与三个公开持续学习基准的大量实验，实证证明了其有效性。