Understanding Deep Convolutional Networks

Stéphane Mallat

doi:10.1098/rsta.2015.0203

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arXiv · 1601.04920

Understanding Deep Convolutional Networks

Stéphane Mallat

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Abstract

Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and non-linearities. A mathematical framework is introduced to analyze their properties. Computations of invariants involve multiscale contractions, the linearization of hierarchical symmetries, and sparse separations. Applications are discussed.

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Stéphane Mallat. 2016-01-19. Understanding Deep Convolutional Networks. https://doi.org/10.1098/rsta.2015.0203

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