Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning

Understand

Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones.

• Balancing flexibility and stability remains a significant challenge, particularly when the task ID is unknown.
• To address this, our study reveals that the gap in feature distribution between novel and existing tasks is primarily driven by differences in mean and covariance moments.
• Building on this insight, we propose a novel semantic drift calibration method that incorporates mean shift compensation and covariance calibration.