Pushing the Limits of Distillation-Based Class-Incremental Learning via Lightweight Plugins

arXiv:2512.03537v2 Announce Type: replace-cross
Abstract: Existing replay and distillation-based class-incremental learning (CIL) methods are effective at retaining past knowledge but are still constrained by the stability-plasticity dilemma. Since their resulting models are learned over a sequence of incremental tasks, they encode rich representations and can be regarded as pre-trained bases. Building on this view, we propose a plug-in extension paradigm termed Deployment of LoRA Components (DLC) to enhance them. For each task, we use Low-Rank Adaptation (LoRA) to inject task-specific residuals into the base model’s deep layers. During inference, representations with task-specific residuals are aggregated to produce classification predictions. To mitigate interference from non-target LoRA plugins, we introduce a lightweight weighting unit. This unit learns to assign importance scores to different LoRA-tuned representations. Like downloadable content in software, DLC serves as a plug-and-play enhancement that efficiently extends the base methods. Remarkably, on the large-scale ImageNet-100, with merely 4% of the parameters of a standard ResNet-18, our DLC model achieves a significant 8% improvement in accuracy, demonstrating exceptional efficiency. Under a fixed memory budget, methods equipped with DLC surpass state-of-the-art expansion-based methods.