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. We make the code available.

翻译：持续学习能够在学习新任务的同时不遗忘先前学过的任务，从而实现正向知识迁移，提升新旧任务的表现。然而，持续学习对可解释性提出了新的挑战，因为模型预测背后的推理依据可能随时间变化，导致可解释性概念漂移。我们通过提出一种基于原型部分的、无需样本的类别增量持续学习方法——可解释的类别增量学习（ICICLE）来解决这一问题。该方法包含三个关键创新点：可解释性正则化技术，在保留用户友好的正向推理的同时蒸馏已学概念；面向细粒度场景的基于邻近性的原型初始化策略；以及针对原型部分的任务近因偏差补偿机制。实验结果表明，ICICLE减少了可解释性概念漂移，并且在基于概念模型的常见类别增量学习中，性能优于现有的无需样本方法。我们已公开相关代码。