Visualizing Deep Networks by Optimizing with Integrated Gradients

Authors

  • Zhongang Qi Oregon State University
  • Saeed Khorram Oregon State University
  • Li Fuxin Oregon State University

DOI:

https://doi.org/10.1609/aaai.v34i07.6863

Abstract

Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. The main novelty of the approach is to compute descent directions based on the integrated gradients instead of the normal gradient, which avoids local optima and speeds up convergence. Compared with previous approaches, our method can flexibly compute heatmaps at any resolution for different user needs. Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches.

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Published

2020-04-03

How to Cite

Qi, Z., Khorram, S., & Fuxin, L. (2020). Visualizing Deep Networks by Optimizing with Integrated Gradients. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11890-11898. https://doi.org/10.1609/aaai.v34i07.6863

Issue

Section

AAAI Technical Track: Vision