Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may change over time, leading to interpretability concept drift. We address this problem by proposing Interpretable Class-InCremental LEarning (ICICLE), an exemplar-free approach that adopts a prototypical part-based approach. It consists of three crucial novelties: interpretability regularization that distills previously learned concepts while preserving user-friendly positive reasoning; proximity-based prototype initialization strategy dedicated to the fine-grained setting; and task-recency bias compensation devoted to prototypical parts. Our experimental results demonstrate that ICICLE reduces the interpretability concept drift and outperforms the existing exemplar-free methods of common class-incremental learning when applied to concept-based models.

翻译：持续学习能够在学习新任务的同时不遗忘先前学到的知识，并促进新旧任务之间的正向知识迁移，从而提升整体性能。然而，持续学习对可解释性提出了新挑战：模型预测背后的推理逻辑可能随时间变化，导致可解释性概念漂移。针对这一问题，我们提出ICICLE（可解释类别增量持续学习），一种基于原型部件方法且无需样本存储的技术。该方法的三个核心创新包括：通过可解释性正则化在保留用户友好的正向推理能力的同时蒸馏先前学到的概念；针对细粒度场景设计的基于邻近度的原型初始化策略；以及专用于原型部件的任务近期偏差补偿机制。实验结果表明，ICICLE有效降低了可解释性概念漂移，且在基于概念模型的场景中，其性能优于现有无需样本存储的通用类别增量学习方法。