论文标题
将反事实与沙普利值相结合以解释图像模型
Combining Counterfactuals With Shapley Values To Explain Image Models
论文作者
论文摘要
随着在敏感应用中广泛使用复杂的机器学习模型，了解他们的决策已成为一项重要任务。对表格数据培训的模型在解释其基本决策过程中的重大进展是由于具有少量的离散功能。但是，将这些方法应用于高维输入（例如图像）并不是一项琐碎的任务。图像由原子水平的像素组成，并不具有任何解释性。在这项工作中，我们试图使用带注释的图像的高级可解释特征来提供解释。我们利用游戏理论的Shapley Value框架，该框架在XAI问题中广泛接受。通过开发一条管道来生成反事实并随后使用它来估计莎普利值，我们获得了具有强大的公理保证的对比度和可解释的解释。
With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in explanations of their underlying decision making processes by virtue of having a small number of discrete features. However, applying these methods to high-dimensional inputs such as images is not a trivial task. Images are composed of pixels at an atomic level and do not carry any interpretability by themselves. In this work, we seek to use annotated high-level interpretable features of images to provide explanations. We leverage the Shapley value framework from Game Theory, which has garnered wide acceptance in general XAI problems. By developing a pipeline to generate counterfactuals and subsequently using it to estimate Shapley values, we obtain contrastive and interpretable explanations with strong axiomatic guarantees.
