Title: Ablation Path Saliency. Abstract: We consider the saliency problem for black-box classification. In image classification, this means highlighting the part of the image that is most relevant for the current decision.
We cast the saliency problem as finding an optimal ablation path between two images. An ablation path consists of a sequence of ever smaller masks, joining the current image to a reference image in another decision region. The optimal path will stay as long as possible in the current decision region. This approach extends the ablation tests in [Sturmfels et al. (2020)]. The gradient of the corresponding objective function is closely related to the integrated gradient method [Sundararajan et al. (2017)]. In the saturated case (when the classifier outputs a binary value) our method would reduce to the meaningful perturbation approach [Fong & Vedaldi (2017)], since crossing the decision boundary as late as
possible would then be equivalent to finding the smallest possible mask lying on
the decision boundary.
Our interpretation provides geometric understanding of existing saliency methods, and suggests a novel approach based on ablation path optimisation.