Abstract:Providing a human-understandable explanation of classifiers' decisions has become imperative to generate trust in their use for dayto-day tasks. Although many works have addressed this problem by generating visual explanation maps, they often provide noisy and inaccurate results forcing the use of heuristic regularization unrelated to the classifier in question. In this paper, we propose a new general perspective of the visual explanation problem overcoming these limitations. We show that visual explanation ca… Show more
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