Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
This addresses the issue of unreliable visual interpretations for users of deep learning models, though it is incremental as it builds on existing saliency map techniques.
The paper tackled the problem of noisy saliency maps in deep neural network interpretation by identifying that noise arises when irrelevant features pass through ReLU activations, and proposed Rectified Gradient, a method that improved saliency map quality and outperformed other attribution methods on CIFAR-10 and ImageNet.
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 hypotheses were proposed to account for this phenomenon, there are few works that provide rigorous analyses of noisy saliency maps. In this paper, we firstly propose a new hypothesis that noise may occur in saliency maps when irrelevant features pass through ReLU activation functions. Then, we propose Rectified Gradient, a method that alleviates this problem through layer-wise thresholding during backpropagation. Experiments with neural networks trained on CIFAR-10 and ImageNet showed effectiveness of our method and its superiority to other attribution methods.