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Saliency Map, the gradient of the score function with respect to the input,is the most basic technique for interpreting deep neural network decisions.However, saliency maps are often visually noisy. Although several hypotheseswere proposed to account for this phenomenon, there are few works that providerigorous analyses of noisy saliency maps. In this paper, we firstly propose anew hypothesis that noise may occur in saliency maps when irrelevant featurespass through ReLU activation functions. Then, we propose Rectified Gradient, amethod that alleviates this problem through layer-wise thresholding duringbackpropagation. Experiments with neural networks trained on CIFAR-10 andImageNet showed effectiveness of our method and its superiority to otherattribution methods.

Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps