让用户参与调整模型的视觉概念，让解释更符合人眼理解。
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
- 用户可交互修改模型使用的视觉概念，避免特征混杂
- 在真实数据集上保持分类准确率，同时提升解释一致性
- 适合需要可解释性且重视个性化理解的研究者
基于概念的可解释神经网络因能提供直观、类比推理式的解释（如“这只鸟看起来像麻雀”）而受到关注。然而，其主要缺陷在于概念不一致：多个视觉特征被错误地合并为一个概念（如将鸟头和翅膀视为单一概念），导致模型推理与人类理解脱节。此外，现有方法无法融入用户的偏好。为此，我们提出YoursProtoP，一种新型交互式策略，可依据用户需求个性化调整模型所用的原型部件（即视觉概念）。通过引入用户监督，YoursProtoP能动态调整并拆分用于预测与解释的概念，使其更契合用户认知。在合成数据集FunnyBirds及真实数据集CUB、CARS、PETS上的全面用户研究中，验证了YoursProtoP在保持模型准确率的同时，显著提升了概念一致性。
原文摘要 · Abstract (English)
Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as "this bird looks like those sparrows". However, a major limitation is that these explanations may not always be comprehensible to users due to concept inconsistency, where multiple visual features are inappropriately mixed (e.g., a bird's head and wings treated as a single concept). This inconsistency breaks the alignment between model reasoning and human understanding. Furthermore, users have specific preferences for how concepts should look, yet current approaches provide no mechanism for incorporating their feedback. To address these issues, we introduce YoursProtoP, a novel interactive strategy that enables the personalization of prototypical parts - the visual concepts used by the model - according to user needs. By incorporating user supervision, YoursProtoP adapts and splits concepts used for both prediction and explanation to better match the user's preferences and understanding. Through experiments on both the synthetic FunnyBirds dataset and a real-world scenario using the CUB, CARS, and PETS datasets in a comprehensive user study, we demonstrate the effectiveness of YoursProtoP in achieving concept consistency without compromising the accuracy of the model.
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。