We introduce a new, systematic framework for visualizing information flow in deep networks. Specifically, given any trained deep convolutional network model and a given test image, our method produces a compact support in the image domain that corresponds to a (high-resolution) feature that contributes to the given explanation. Our method is both computationally efficient as well as numerically robust. We present several preliminary numerical results that support the benefits of our framework over existing methods.
@article{arxiv.1711.06221,
title = {A Forward-Backward Approach for Visualizing Information Flow in Deep Networks},
author = {Aditya Balu and Thanh V. Nguyen and Apurva Kokate and Chinmay Hegde and Soumik Sarkar},
journal= {arXiv preprint arXiv:1711.06221},
year = {2017}
}
Comments
Presented at NIPS 2017 Symposium on Interpretable Machine Learning