提出概率框架统一生成式解释与可解释性研究
Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
- 用概率建模统一生成式解释的理论基础
- 确保生成结果符合可解释性核心标准
- 促进生成与可解释性领域深度交流
近期多项研究利用深度生成模型生成图像分类器的例子化解释。尽管视觉效果惊艳，但这些方法与经典可解释性文献存在概念和沟通鸿沟，导致目标与期望错位。本文通过提出一种概率框架，以概率方式形式化定义例子化解释，使其可通过深度生成模型建模，同时与可解释性领域广泛接受的核心特征和理想要求保持一致。一方面，为构建扎实的生成式例子化解释算法提供结构化框架；另一方面，促进生成与可解释性研究社区间的沟通，提升严谨性与透明度，改善同行讨论质量，推动该方向的研究进展。
原文摘要 · Abstract (English)
Recently, several methods have leveraged deep generative modeling to produce example-based explanations of image classifiers. Despite producing visually stunning results, these methods are largely disconnected from classical explainability literature. This conceptual and communication gap leads to misunderstandings and misalignments in goals and expectations. In this paper, we bridge this gap by proposing a probabilistic framework for example-based explanations, formally defining the example-based explanations in a probabilistic manner amenable for modeling via deep generative models while coherent with the critical characteristics and desiderata widely accepted in the explainability community. Our aim is on one hand to provide a constructive framework for the development of well-grounded generative algorithms for example-based explanations and, on the other, to facilitate communication between the generative and explainability research communities, foster rigor and transparency, and improve the quality of peer discussion and research progress in this promising direction.
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