评估可解释部件原型图像分类器的Co-12方法
The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers
AI总结：
本文基于Co-12属性，综述了可解释部件原型图像分类器的解释质量评估方法，揭示了研究空白，并提供了速查表以推动该领域发展。
摘要：
可解释部件原型模型是设计上可解释的计算机视觉模型。这些模型学习原型部件并在图像中识别这些组件，从而结合了分类和解释。尽管内在可解释模型近期受到关注，但缺乏对可解释部件原型模型解释质量评估的全面概述。基于arXiv:2201.08164提出的Co-12解释质量属性（如正确性、完整性、紧凑性），我们回顾了评估部件原型模型的现有工作，揭示了研究空白，并概述了评估部件原型模型解释质量的未来方法。因此，本文促进了可解释部件原型模型这一相对较新研究领域的进展与成熟。我们还提供了一份“Co-12速查表”，作为评估部件原型模型发现的简明总结。
英文摘要：
Interpretable part-prototype models are computer vision models that are explainable by design. The models learn prototypical parts and recognise these components in an image, thereby combining classification and explanation. Despite the recent attention for intrinsically interpretable models, there is no comprehensive overview on evaluating the explanation quality of interpretable part-prototype models. Based on the Co-12 properties for explanation quality as introduced in arXiv:2201.08164 (e.g., correctness, completeness, compactness), we review existing work that evaluates part-prototype models, reveal research gaps and outline future approaches for evaluation of the explanation quality of part-prototype models. This paper, therefore, contributes to the progression and maturity of this relatively new research field on interpretable part-prototype models. We additionally provide a ``Co-12 cheat sheet'' that acts as a concise summary of our findings on evaluating part-prototype models.
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。
