In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.
@article{arxiv.1704.04133,
title = {Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks},
author = {Devinder Kumar and Alexander Wong and Graham W. Taylor},
journal= {arXiv preprint arXiv:1704.04133},
year = {2017}
}
Comments
Accepted at Computer Vision and Patter Recognition Workshop (CVPR-W) on Explainable Computer Vision, 2017