SmoothGrad: removing noise by adding noise

Autor:	Smilkov, Daniel, Thorat, Nikhil, Kim, Been, Viégas, Fernanda, Wattenberg, Martin
Rok vydání:	2017
Předmět:	Computer Science - Learning Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1706.03825 Zobrazit plný text záznamu View this record from Arxiv