  Providing a human-understandable explanation of classifiers' decisions has
become imperative to generate trust in their use for day-to-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 can be produced as the
difference between two generated images obtained via two specific conditional
generative models. Both generative models are trained using the classifier to
explain and a database to enforce the following properties: (i) All images
generated by the first generator are classified similarly to the input image,
whereas the second generator's outputs are classified oppositely. (ii)
Generated images belong to the distribution of real images. (iii) The distances
between the input image and the corresponding generated images are minimal so
that the difference between the generated elements only reveals relevant
information for the studied classifier. Using symmetrical and cyclic
constraints, we present two different approximations and implementations of the
general formulation. Experimentally, we demonstrate significant improvements
w.r.t the state-of-the-art on three different public data sets. In particular,
the localization of regions influencing the classifier is consistent with human
annotations.
