A Learning Paradigm for Interpretable Gradients

Autor:	Figueroa, Felipe Torres, Zhang, Hanwei, Sicre, Ronan, Avrithis, Yannis, Ayache, Stephane
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: VISAPP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.15024 Zobrazit plný text záznamu View this record from Arxiv