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

Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization