CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning

TMLR Paper6665 Authors

Abstract:

Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a new task. This issue is especially pronounced in class incremental learning (CIL), which is the most challenging setting in continual learning. Existing methods to address catastrophic forgetting often sacrifice either model interpretability or accuracy. To address this challenge, we introduce Class-Incremental Concept Bottleneck Model (CI-CBM), which leverage novel techniques, including concept regularization and pseudo-concept generation to maintain interpretable decision processes throughout incremental learning phases.
Through extensive evaluation on seven benchmark datasets, CI-CBM achieves comparable performance to black-box models and significantly outperforms previous interpretable approaches in CIL, with an average 36\% accuracy gain.
CI-CBM provides both interpretable decisions on individual inputs and understandable global decision rules, as shown in our experiments, thereby demonstrating that human-understandable concepts can be maintained during incremental learning without compromising model performance.
Our approach is effective in both pretrained and non-pretrained scenarios; in the latter, the backbone is trained from scratch during the first learning phase.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Dmitry_Kangin1

Submission Number: 6665