超越补丁：挖掘可解释的部分原型用于可解释AI
Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI
AI总结：
PCMNet通过挖掘可解释的部分原型提升AI系统的可解释性、稳定性和鲁棒性，为构建可靠且一致的AI系统提供新方法。
摘要：
随着AI系统能力的提升，其决策的可理解性和与人类期望的一致性变得愈发重要。一个关键挑战是深度模型的可解释性有限。事后方法如GradCAM提供热图但提供有限的概念洞察，而基于原型的方法提供基于示例的解释但往往依赖于刚性的区域选择且缺乏语义一致性。为了解决这些限制，我们提出了PCMNet，一个部分原型概念挖掘网络，该网络从有意义的图像区域中学习人类可理解的原型，而无需额外监督。通过将这些原型聚类为概念组并提取概念激活向量，PCMNet提供结构化的、概念层面的解释，并增强了对遮挡和挑战性条件的鲁棒性，这对于构建可靠且一致的AI系统至关重要。在多个图像分类基准上的实验表明，PCMNet在可解释性、稳定性及鲁棒性方面均优于现有最先进方法。这项工作通过增强AI系统的透明度、可控性和可信度，为AI对齐做出了贡献。我们的代码可在：https://github.com/alehdaghi/PCMNet上找到。
英文摘要：
As AI systems grow more capable, it becomes increasingly important that their decisions remain understandable and aligned with human expectations. A key challenge is the limited interpretability of deep models. Post-hoc methods like GradCAM offer heatmaps but provide limited conceptual insight, while prototype-based approaches offer example-based explanations but often rely on rigid region selection and lack semantic consistency. To address these limitations, we propose PCMNet, a part-prototypical concept mining network that learns human-comprehensible prototypes from meaningful image regions without additional supervision. By clustering these prototypes into concept groups and extracting concept activation vectors, PCMNet provides structured, concept-level explanations and enhances robustness to occlusion and challenging conditions, which are both critical for building reliable and aligned AI systems. Experiments across multiple image classification benchmarks show that PCMNet outperforms state-of-the-art methods in interpretability, stability, and robustness. This work contributes to AI alignment by enhancing transparency, controllability, and trustworthiness in AI systems. Our code is available at: https://github.com/alehdaghi/PCMNet.
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。
