  Explaining the output of a deep network remains a challenge. In the case of
an image classifier, one type of explanation is to identify pixels that
strongly influence the final decision. A starting point for this strategy is
the gradient of the class score function with respect to the input image. This
gradient can be interpreted as a sensitivity map, and there are several
techniques that elaborate on this basic idea. This paper makes two
contributions: it introduces SmoothGrad, a simple method that can help visually
sharpen gradient-based sensitivity maps, and it discusses lessons in the
visualization of these maps. We publish the code for our experiments and a
website with our results.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning