Reducing Checkerboard Noise in Deep Saliency Maps for Improved Model Interpretability

The core message of this paper is to investigate methods to reduce the checkerboard noise in deep saliency maps coming from convolutional downsampling, in order to make deep learning models more interpretable for gradient-based saliency maps computed in hidden layers.