Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness
This work addresses the challenge of enabling models to learn new tasks without forgetting old ones in continual learning, with incremental improvements for AI systems requiring adaptability.
The paper tackles the problem of improving forward compatibility in Class Incremental Learning by introducing an effective-Rank based Feature Richness enhancement method, which increases representation rank during the base session to enhance novel task performance and mitigate catastrophic forgetting, achieving improved average incremental accuracy across eleven methods.
Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining knowledge obtained from prior tasks. Although previous studies have predominantly focused on backward compatible approaches to mitigate catastrophic forgetting, recent investigations have introduced forward compatible methods to enhance performance on novel tasks and complement existing backward compatible methods. In this study, we introduce an effective-Rank based Feature Richness enhancement (RFR) method, designed for improving forward compatibility. Specifically, this method increases the effective rank of representations during the base session, thereby facilitating the incorporation of more informative features pertinent to unseen novel tasks. Consequently, RFR achieves dual objectives in backward and forward compatibility: minimizing feature extractor modifications and enhancing novel task performance, respectively. To validate the efficacy of our approach, we establish a theoretical connection between effective rank and the Shannon entropy of representations. Subsequently, we conduct comprehensive experiments by integrating RFR into eleven well-known CIL methods. Our results demonstrate the effectiveness of our approach in enhancing novel-task performance while mitigating catastrophic forgetting. Furthermore, our method notably improves the average incremental accuracy across all eleven cases examined.