CIP-Net让模型持续学习新任务时不忘旧知识，还能自动生成解释。
CIP-Net: Continual Interpretable Prototype-based Network
- 用原型机制实现无样本自解释，不存历史数据。
- 在任务和类别增量场景下均达顶尖性能，内存开销极低。
- 适合需要可解释性与高效部署的持续学习场景。
持续学习要求模型在不遗忘旧任务的前提下逐步学习新任务，其核心挑战是灾难性遗忘。近年来，可解释AI被视为缓解遗忘的有效途径，尤其是自解释模型能在预测时生成解释，有助于知识保留。然而，现有方法多依赖事后解释或为每个新任务额外存储记忆，难以扩展。本文提出CIP-Net，一种无示例、自解释的原型基持续学习模型。该模型无需存储历史样本，保持轻量架构，同时提供有效解释并维持优异性能。实验表明，CIP-Net在任务增量和类别增量设置下均优于现有无示例与自解释方法，且内存开销显著更低，是一种兼具实用性与可解释性的持续学习解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。