A Learning Paradigm for Interpretable Gradients

TL;DR

This paper introduces a new training method that regularizes gradients to be less noisy and more interpretable, enhancing the quality of saliency maps for convolutional networks.

Contribution

It proposes a novel regularization loss that aligns standard backpropagation gradients with guided backpropagation, improving interpretability.

Findings

Gradients become less noisy and more interpretable.

Quantitative improvements in interpretability metrics.

Enhanced visualization quality across different networks.

Abstract

This paper studies interpretability of convolutional networks by means of saliency maps. Most approaches based on Class Activation Maps (CAM) combine information from fully connected layers and gradient through variants of backpropagation. However, it is well understood that gradients are noisy and alternatives like guided backpropagation have been proposed to obtain better visualization at inference. In this work, we present a novel training approach to improve the quality of gradients for interpretability. In particular, we introduce a regularization loss such that the gradient with respect to the input image obtained by standard backpropagation is similar to the gradient obtained by guided backpropagation. We find that the resulting gradient is qualitatively less noisy and improves quantitatively the interpretability properties of different networks, using several interpretability methods.