SmoothGrad is a method to reduce visual noise in gradient-based sensitivity maps for deep neural networks. The paper introduces SmoothGrad, which averages sensitivity maps from multiple noisy versions of an input image to produce smoother, more interpretable maps. It also discusses the benefits of adding noise during training, which can further reduce noise in sensitivity maps. The method is compared to other gradient-based sensitivity map techniques, and it is shown to produce more visually coherent and discriminative results. The paper also provides visualization techniques and discusses how to improve the interpretation of sensitivity maps. SmoothGrad is implemented and made available online, along with 200+ examples of each method. The results suggest that smoothing gradients can lead to more meaningful insights into how neural networks make decisions. The paper also discusses the importance of interpretability in machine learning models and the challenges of understanding how complex models function.SmoothGrad is a method to reduce visual noise in gradient-based sensitivity maps for deep neural networks. The paper introduces SmoothGrad, which averages sensitivity maps from multiple noisy versions of an input image to produce smoother, more interpretable maps. It also discusses the benefits of adding noise during training, which can further reduce noise in sensitivity maps. The method is compared to other gradient-based sensitivity map techniques, and it is shown to produce more visually coherent and discriminative results. The paper also provides visualization techniques and discusses how to improve the interpretation of sensitivity maps. SmoothGrad is implemented and made available online, along with 200+ examples of each method. The results suggest that smoothing gradients can lead to more meaningful insights into how neural networks make decisions. The paper also discusses the importance of interpretability in machine learning models and the challenges of understanding how complex models function.