Online Continual Learning (OCL) empowers machine learning models to acquire new knowledge online across a sequence of tasks. However, OCL faces a significant challenge: catastrophic forgetting, wherein the model learned in previous tasks is substantially overwritten upon encountering new tasks, leading to a biased forgetting of prior knowledge. Moreover, the continual doman drift in sequential learning tasks may entail the gradual displacement of the decision boundaries in the learned feature space, rendering the learned knowledge susceptible to forgetting. To address the above problem, in this paper, we propose a novel rehearsal strategy, termed Drift-Reducing Rehearsal (DRR), to anchor the domain of old tasks and reduce the negative transfer effects. First, we propose to select memory for more representative samples guided by constructed centroids in a data stream. Then, to keep the model from domain chaos in drifting, a two-level angular cross-task Contrastive Margin Loss (CML) is proposed, to encourage the intra-class and intra-task compactness, and increase the inter-class and inter-task discrepancy. Finally, to further suppress the continual domain drift, we present an optional Centorid Distillation Loss (CDL) on the rehearsal memory to anchor the knowledge in feature space for each previous old task. Extensive experimental results on four benchmark datasets validate that the proposed DRR can effectively mitigate the continual domain drift and achieve the state-of-the-art (SOTA) performance in OCL.

翻译：在线持续学习（Online Continual Learning, OCL）使机器学习模型能够跨任务序列在线获取新知识。然而，OCL面临一个重要挑战：灾难性遗忘，即模型在先前任务中学到的知识在遇到新任务时被大量覆盖，导致先前知识的偏向性遗忘。此外，序列学习任务中持续的领域漂移可能导致学习特征空间中的决策边界逐渐偏移，使已学知识易被遗忘。为解决上述问题，本文提出一种新颖的复述策略——漂移减少复述（Drift-Reducing Rehearsal, DRR），以锚定旧任务领域并减少负迁移效应。首先，我们提出一种基于数据流中构造质心引导的更具代表性样本的记忆选择方法。其次，为保持模型在漂移中免受领域混乱影响，提出一种两级角度的跨任务对比边界损失（Contrastive Margin Loss, CML），以促进类内和任务内紧凑性，并增大类间和任务间差异性。最后，为进一步抑制持续领域漂移，我们在复述记忆上引入可选的质心蒸馏损失（Centroid Distillation Loss, CDL），以在特征空间中锚定每个先前旧任务的知识。在四个基准数据集上的大量实验结果表明，所提出的DRR能有效缓解持续领域漂移，并在OCL中实现最先进（SOTA）性能。