CIP-Net: Continual Interpretable Prototype-based Network

• URL: http://arxiv.org/abs/2512.07981v1
• Date: Mon, 08 Dec 2025 19:13:19 GMT
• Title: CIP-Net: Continual Interpretable Prototype-based Network
• Authors: Federico Di Valerio, Michela Proietti, Alessio Ragno, Roberto Capobianco,
• Abstract summary: Continual learning constrains models to learn new tasks over time without forgetting what they have already learned.<n>We introduce CIP-Net, an exemplar-free self-explainable prototype-based model designed for continual learning.
• Score: 3.0098885383612104
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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