# SmoothGrad: removing noise by adding noise

arXiv: https://arxiv.org/abs/1706.03825 · HTML: https://mlsanity.com/paper/1706.03825/

## Key facts

- Cited by 1,109 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2017.

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## Abstract (arXiv)

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.