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An Inside Look at Deep Neural Networks using Graph Signal Processing

Abstract : Deep Neural Networks (DNNs) are state-of-the-art in many machine learning benchmarks. Understanding how they perform is a major open question. In this paper, we are interested in using graph signal processing to monitor the intermediate representations obtained in a simple DNN architecture. We compare different metrics and measures and show that smoothness of label signals on k-nearest neighbor graphs are a good candidate to interpret individual layers role in achieving good performance.
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https://hal.inria.fr/hal-01959770
Contributor : Benjamin Girault <>
Submitted on : Wednesday, December 19, 2018 - 1:23:57 AM
Last modification on : Saturday, May 1, 2021 - 3:52:35 AM

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Vincent Gripon, Antonio Ortega, Benjamin Girault. An Inside Look at Deep Neural Networks using Graph Signal Processing. 2018 Information Theory and Applications Workshop (ITA), Feb 2018, San Diego, CA, United States. pp.1-9, ⟨10.1109/ITA.2018.8503214⟩. ⟨hal-01959770⟩

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