  Image classifiers are known to be difficult to interpret and therefore
require explanation methods to understand their decisions. We present
ShearletX, a novel mask explanation method for image classifiers based on the
shearlet transform -- a multiscale directional image representation. Current
mask explanation methods are regularized by smoothness constraints that protect
against undesirable fine-grained explanation artifacts. However, the smoothness
of a mask limits its ability to separate fine-detail patterns, that are
relevant for the classifier, from nearby nuisance patterns, that do not affect
the classifier. ShearletX solves this problem by avoiding smoothness
regularization all together, replacing it by shearlet sparsity constraints. The
resulting explanations consist of a few edges, textures, and smooth parts of
the original image, that are the most relevant for the decision of the
classifier. To support our method, we propose a mathematical definition for
explanation artifacts and an information theoretic score to evaluate the
quality of mask explanations. We demonstrate the superiority of ShearletX over
previous mask based explanation methods using these new metrics, and present
exemplary situations where separating fine-detail patterns allows explaining
phenomena that were not explainable before.
