A Learning Paradigm for Interpretable Gradients
This work addresses interpretability issues in deep learning for researchers and practitioners, but it is incremental as it builds on existing gradient-based methods.
The paper tackles the problem of noisy gradients in convolutional networks for interpretability by introducing a novel training approach that regularizes standard backpropagation gradients to resemble guided backpropagation gradients, resulting in qualitatively less noisy gradients and improved interpretability properties across various networks and methods.
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.