Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors From a Bi-level Optimization...

In online continual learning (CL), models trained on changing distributions easily forget previously learned knowledge and bias toward newly received tasks. To address this issue, we present Continual Bias Adaptor (CBA), a bi-level framework that augments the classification network to adapt to catastrophic distribution shifts during training, achieving a stable consolidation of all seen tasks. However, CBA adjusts distribution shifts in a class-specific manner, exacerbating the stability gap issue and fails to meet the need for continual testing to some extent. To mitigate this challenge, we further propose a novel class-agnostic CBA module that separately aggregates the posterior probabilities of new and old tasks, applying a stable adjustment to the results. We combine these two kinds of CBA modules into a unified Dual-CBA module, which thus is capable of adapting to catastrophic distribution shifts and simultaneously meets the real-time testing requirements of online CL. Besides, we propose Incremental Batch Normalization (IBN), a tailored BN module to re-estimate its population statistics for alleviating the feature bias arising from our bi-level framework. We theoretically provide some insights into how it mitigates distribution shifts, and empirically demonstrate its superiority through extensive experiments based on four rehearsal-based baselines and three public CL benchmarks.