  Neural network visualization techniques mark image locations by their
relevancy to the network's classification. Existing methods are effective in
highlighting the regions that affect the resulting classification the most.
However, as we show, these methods are limited in their ability to identify the
support for alternative classifications, an effect we name {\em the saliency
bias} hypothesis. In this work, we integrate two lines of research:
gradient-based methods and attribution-based methods, and develop an algorithm
that provides per-class explainability. The algorithm back-projects the per
pixel local influence, in a manner that is guided by the local attributions,
while correcting for salient features that would otherwise bias the
explanation. In an extensive battery of experiments, we demonstrate the ability
of our methods to class-specific visualization, and not just the predicted
label. Remarkably, the method obtains state of the art results in benchmarks
that are commonly applied to gradient-based methods as well as in those that
are employed mostly for evaluating attribution methods. Using a new
unsupervised procedure, our method is also successful in demonstrating that
self-supervised methods learn semantic information.
