CIP-Net：持续可解释原型网络
CIP-Net: Continual Interpretable Prototype-based Network
AI总结：
CIP-Net是一种无需存储示例的自我可解释原型模型，通过简单架构实现持续学习中的高性能与低开销。
摘要：
持续学习要求模型在时间推移中不断学习新任务，同时不遗忘已学内容。这一场景下的关键挑战是灾难性遗忘，即学习新信息会导致模型在先前任务上的性能下降。最近，可解释AI被提出作为改善理解和减少遗忘的有希望方法。特别是，自我可解释模型因其在预测过程中生成解释而有益，有助于保持知识。然而，大多数现有可解释方法使用事后解释或需要为每个新任务额外存储记忆，导致可扩展性有限。在本工作中，我们引入了CIP-Net，一种无需示例的自我可解释原型模型，专为持续学习设计。CIP-Net不存储过往示例，保持简单架构，同时仍能提供有用的解释和强性能。我们证明CIP-Net在任务增量和类别增量设置中相比先前无示例和自我可解释方法实现了最先进的性能，同时具有显著更低的记忆相关开销。这使其成为持续学习的实用且可解释的解决方案。
英文摘要：
Continual learning constrains models to learn new tasks over time without forgetting what they have already learned. A key challenge in this setting is catastrophic forgetting, where learning new information causes the model to lose its performance on previous tasks. Recently, explainable AI has been proposed as a promising way to better understand and reduce forgetting. In particular, self-explainable models are useful because they generate explanations during prediction, which can help preserve knowledge. However, most existing explainable approaches use post-hoc explanations or require additional memory for each new task, resulting in limited scalability. In this work, we introduce CIP-Net, an exemplar-free self-explainable prototype-based model designed for continual learning. CIP-Net avoids storing past examples and maintains a simple architecture, while still providing useful explanations and strong performance. We demonstrate that CIPNet achieves state-of-the-art performances compared to previous exemplar-free and self-explainable methods in both task- and class-incremental settings, while bearing significantly lower memory-related overhead. This makes it a practical and interpretable solution for continual learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
