  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.
