2021
DOI: 10.48550/arxiv.2108.05312
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Towards Interpretable Deep Networks for Monocular Depth Estimation

Abstract: Deep networks for Monocular Depth Estimation (MDE) have achieved promising performance recently and it is of great importance to further understand the interpretability of these networks. Existing methods attempt to provide posthoc explanations by investigating visual cues, which may not explore the internal representations learned by deep networks. In this paper, we find that some hidden units of the network are selective to certain ranges of depth, and thus such behavior can be served as a way to interpret t… Show more

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