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Saliency methods compute heat maps that highlight portions of an input thatwere most {\em important} for the label assigned to it by a deep net.Evaluations of saliency methods convert this heat map into a new {\em maskedinput} by retaining the $k$ highest-ranked pixels of the original input andreplacing the rest with \textquotedblleft uninformative\textquotedblright\pixels, and checking if the net's output is mostly unchanged. This is usuallyseen as an {\em explanation} of the output, but the current paper highlightsreasons why this inference of causality may be suspect. Inspired by logicconcepts of {\em completeness \& soundness}, it observes that the above type ofevaluation focuses on completeness of the explanation, but ignores soundness.New evaluation metrics are introduced to capture both notions, while staying inan {\em intrinsic} framework -- i.e., using the dataset and the net, but noseparately trained nets, human evaluations, etc. A simple saliency method isdescribed that matches or outperforms prior methods in the evaluations.Experiments also suggest new intrinsic justifications, based on soundness, forpopular heuristic tricks such as TV regularization and upsampling.

New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound