  Saliency methods compute heat maps that highlight portions of an input that
were 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 masked
input} by retaining the $k$ highest-ranked pixels of the original input and
replacing the rest with \textquotedblleft uninformative\textquotedblright\
pixels, and checking if the net's output is mostly unchanged. This is usually
seen as an {\em explanation} of the output, but the current paper highlights
reasons why this inference of causality may be suspect. Inspired by logic
concepts of {\em completeness \& soundness}, it observes that the above type of
evaluation focuses on completeness of the explanation, but ignores soundness.
New evaluation metrics are introduced to capture both notions, while staying in
an {\em intrinsic} framework -- i.e., using the dataset and the net, but no
separately trained nets, human evaluations, etc. A simple saliency method is
described that matches or outperforms prior methods in the evaluations.
Experiments also suggest new intrinsic justifications, based on soundness, for
popular heuristic tricks such as TV regularization and upsampling.
