English(EN) Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations

概念瓶颈模型：探索可解释性与鲁棒性的权衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

一篇新的研究论文探讨了概念瓶颈模型（CBMs）的鲁棒性，这类模型旨在提高可解释性。该研究认为，先前关于CBM鲁棒性的发现相互矛盾，是因为混淆了不同的鲁棒性概念和扰动类型。研究人员引入了一个框架，用于在几何和语义扰动下比较CBMs与标准分类器，发现可解释性并不天然保证鲁棒性。相反，概念瓶颈重新分配了敏感性，揭示了可解释性与鲁棒性之间的权衡，这种权衡取决于任务结构和扰动模式。 AI

影响阐明了模型可解释性与鲁棒性之间的关系，表明可解释性并不自动带来更好的鲁棒性。

排序理由一篇发表在arXiv上的研究论文，详细介绍了一个用于评估概念瓶颈模型鲁棒性的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Hanwei Zhang, Tianma Hu, Gaojie Jin, Xu Cheng, Ronghui Mu · 2026-10-01 04:00

可解释但脆弱？几何语义扰动下概念瓶颈的鲁棒性

arXiv:2609.38625v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) are designed to provide interpretable intermediate representations, yet how such bottlenecks affect robustness remains unclear, with existing studies reporting mixed and sometimes contradictory fin…

本文如何被排名

报道来源 [1]

可解释但脆弱？几何语义扰动下概念瓶颈的鲁棒性

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