Mitigating Catastrophic Forgetting in Online Continual Learning With Dual-Margin Contrastive Replay

Online Continual Learning (OCL) enables machine learning models to learn from a stream of non-stationary tasks, making it more aligned with real-world scenarios. 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. Among various OCL strategies, replay-based methods have proven particularly effective in mitigating catastrophic forgetting by maintaining a small buffer of past samples and retraining them alongside new data. However, due to strict memory constraints, these replay buffers often fail to adequately represent the true data distribution of previous tasks. This leads to distributional shifts in the feature space, amplifying forgetting and degrading model performance. To address the problem, in this paper, we propose a novel replay strategy, termed Dual-Margin Contrastive Replay (DMCR), to anchor the distribution 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 distribution chaos in biased replay, 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 distributional drift, we present an optional Centroid Distillation Loss (CDL) on the replay memory to anchor the knowledge in feature space for each previous old task. Extensive experimental results on five benchmark datasets validate that the proposed DMCR can effectively mitigate the catastrophic forgetting and achieve state-of-the-art (SOTA) performance in OCL.