Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transformation and then perform gradient ascent in these coordinates to find counterfactuals which are classified with great confidence as a specified target class. We propose two methods to leverage generative models to construct such suitable coordinate systems that are either exactly or approximately diffeomorphic. We analyze the generation process theoretically using Riemannian differential geometry and validate the quality of the generated counterfactuals using various qualitative and quantitative measures.
翻译:反事实可以以人类可解释的方式解释神经网络的分类决定。 我们提出一种简单而有效的方法来产生这种反事实。 更具体地说, 我们进行适当的二变形协调变换, 然后在这些坐标上进行梯度增高, 以找到极有信心地被归类为特定目标类别的反事实。 我们提出两种方法来利用基因模型来建立这种精确或近似二变形的合适协调系统。 我们从理论上使用里曼差异几何法分析生成过程, 并使用各种定性和定量措施验证产生的反事实的质量。