Closing the Stability–Plasticity Loop: Adaptive Capacity Renewal for Class-Incremental Learning

Class-Incremental Learning (CIL) requires a model to acquire new classes while preserving a usable representation for earlier classes. Existing approaches usually treat this as either a capacity problem (making parameters available for new classes) or a representation problem (constraining feature drift), leaving the two decisions weakly coupled. We formulate the problem as a closed-loop stability–plasticity control problem and propose Closing the Stability–Plasticity Loop (CSPL). CSPL uses one utility-and-reference loop: a maturity-aware utility signal identifies channels whose capacity has become ineffective; function-preserving replacement recycles only those channels; and a shared spatial context memory provides a task-agnostic reference for the resulting features. The reference is enforced through the task-agnostic/task-specific branches and feature distillation, so that capacity renewal is performed without changing the function seen by the stabilizing pathway. Experiments on CIFAR100, Tiny-ImageNet, ImageNet100, and CUB200 show consistent improvements over strong baselines. Under the 50–10 protocol, CSPL improves Last accuracy over the strongest listed baseline by 2.16 percentage points on CIFAR100 and 3.51 points on ImageNet100.