CoMove: Evolving Supervision for Class-Incremental Learning

Class-incremental learning must accommodate new classes while retaining knowledge supported by limited past data. As the classifier expands and replay becomes sparse, both the geometry of learning targets and the evidence supporting them change. We introduce CoMove, a method that adapts the geometry and allocation of supervision throughout learning. For progressive equiangular tight frame (ETF) classifiers, we characterize the displacement of inherited prototypes and derive a spherical transport that aligns teacher references with the expanded geometry while preserving rankings among old classes. Peer, prototype, and transported-teacher relations are integrated through a common objective with shared negative competition and source-specific responses. Class composition and replay support determine their contributions, transferring under-supported supervision to the teacher while preserving total query weight. A single allocation rule applies across datasets and replay budgets, without tuning source weights; inference uses the ETF directly. Experiments on CIFAR-10, CIFAR-100, and TinyImageNet demonstrate consistent improvements over competing methods across low-memory settings. CoMove achieves the highest mean final class-incremental accuracy in all six dataset–buffer settings, with gains over the best baseline in each setting averaging 4.69 percentage points, alongside substantially reduced forgetting. The same incremental objective also supports learning without exemplar replay. Our code is provided in the supplementary material.