Empirical Study of the Topology and Geometry of Deep Networks

CVPR, pp. 3762-3770, 2018.

Cited by: 84|Bibtex|Views14
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Other Links: dblp.uni-trier.de|academic.microsoft.com

Abstract:

The goal of this paper is to analyze the geometric properties of deep neural network image classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. Through a systematic empirical study, we show that state-of-the-art deep nets learn...More

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