Reducing Task-Transition Interference for Exemplar-Free Class-Incremental Learning

Exemplar-free class-incremental learning (EFCIL) requires models to learn new classes while preserving the decision boundaries of previously learned classes. Many EFCIL methods retain compact statistics that summarize previously learned classes. As new tasks arrive, these statistics are primarily used to preserve or correct existing class structure. Yet this preservation-oriented use leaves open a key question: how should retained knowledge guide adaptation to new classes while preserving the old-class decision structure? Without such guidance, new-class updates may overlap with old-class discriminative directions and cause interference. To answer this question, we propose AnchorScale, which turns retained knowledge into an explicit reference for new-class adaptation. Specifically, Fixed Anchoring extracts current-task prototypes with the frozen previous encoder and projects each prototype onto the orthogonal complement of the subspace spanned by retained old-class classifier directions. The resulting directions remain fixed throughout current-task training while the feature extractor is optimized with classification and distillation objectives. At task boundaries, Probability Feedback calibrates the logit scale for the next task based on the teacher’s probability mass over likely old classes, maintaining effective cross-task supervision as the label space expands. Experiments on standard, fine-grained, and large-scale EFCIL benchmarks show that AnchorScale performs competitively with existing methods. Ablation studies confirm the importance of fixed prototype-guided directions and the complementary contribution of Probability Feedback.