# 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/en/paper/1706.03825v1/

## Key points

- The paper addresses the challenge of explaining deep neural networks in image classification.
- One approach is to identify influential pixels by analyzing the gradient of the class score function.
- The paper introduces a method called SmoothGrad to enhance and sharpen sensitivity maps.
- SmoothGrad achieves this by adding random noise and computing multiple gradients for each noisy version.
- Averaging these gradients reduces noise and provides clearer visualizations of important pixels.
- The paper discusses techniques that build upon using gradients as sensitivity maps, highlighting their strengths and limitations.
- Code for experimentation purposes is available, along with a website to access results.
- Overall, SmoothGrad improves our understanding of deep neural networks' decision-making process.

## Summary

The paper titled "SmoothGrad: removing noise by adding noise" addresses the challenge of explaining the output of deep neural networks, particularly in the context of image classification. One approach to explain the decision-making process of an image classifier is to identify the pixels that have a strong influence on the final decision. This can be achieved by analyzing the gradient of the class score function with respect to the input image, which can be interpreted as a sensitivity map. The paper introduces a new method called SmoothGrad, which aims to enhance and sharpen gradient-based sensitivity maps. SmoothGrad achieves this by adding random noise to the input image and computing multiple gradients for each noisy version. By averaging these gradients, SmoothGrad effectively reduces noise and provides clearer visualizations of important pixels. In addition to presenting SmoothGrad, the paper also discusses important lessons learned in visualizing sensitivity maps. It highlights various techniques that build upon the basic idea of using gradients as sensitivity maps and provides insights into their strengths and limitations. To support reproducibility and further research, the authors have made their code available for experimentation purposes. They have also created a website where users can access their results. Overall, this paper contributes to improving our understanding of deep neural networks by introducing SmoothGrad as a method to enhance gradient-based sensitivity maps. The insights provided in this work can help researchers and practitioners better interpret and visualize the decision-making process of image classifiers.