# SmoothGrad: removing noise by adding noise

**Authors:** Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, Martin Wattenberg  
**arXiv:** [1706.03825](https://arxiv.org/abs/1706.03825)  
**Date:** 2017-06-12  
**Summary page:** https://www.summarizepaper.com/fr/paper/1706.03825v1/

## Abstract

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.