Dynamic Concept Pool with Instance-Adaptive Gating for Interpretable Class-Incremental Learning

Continual learning (CL) aims to acquire new knowledge from evolving tasks while preserving previously learned knowledge. Interpretable CL has attracted increasing attention for its potential to achieve strong continual learning performance while providing more transparent decision processes. However, existing methods often construct semantic concepts only once per incremental stage, limiting their adaptability to evolving class relationships, while final predictions still rely on trainable classification modules that may accumulate optimization errors over time. To address these limitations, we propose Dynamic Concept Pool with Instance-Adaptive Gating (DyCoG), an interpretable continual learning framework that grounds both knowledge accumulation and classification in explicit semantic concepts. Specifically, the Dynamic Concept Pool (DCP) progressively refines a human-understandable class-level concept space through classification feedback as new classes are introduced, enabling adaptive and interpretable knowledge accumulation. The Instance-Adaptive Concept Gate (IACG) further selects an instance-specific subset of concepts for each input, making the final decision directly traceable to instance-relevant semantic evidence. The resulting concept representations are directly used for statistical classification without relying on trainable classification modules for final prediction. Experiments on four benchmark datasets demonstrate that DyCoG delivers strong continual learning performance with competitive computational cost. More notably, as task partitions become finer, DyCoG exhibits an overall performance improvement rather than degradation, in contrast to the declining or saturating trends observed in most compared methods.