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理解深度卷积网络。

Understanding deep convolutional networks.

作者信息

Mallat Stéphane

机构信息

École Normale Supérieure, CNRS, PSL, 45 rue d'Ulm, Paris, France

出版信息

Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150203. doi: 10.1098/rsta.2015.0203.

DOI:10.1098/rsta.2015.0203
PMID:26953183
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4792410/
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 nonlinearities. A mathematical framework is introduced to analyse their properties. Computations of invariants involve multiscale contractions with wavelets, the linearization of hierarchical symmetries and sparse separations. Applications are discussed.

摘要

深度卷积网络在许多高维问题上提供了最先进的分类和回归结果。我们回顾了它们的架构，该架构通过一系列线性滤波器权重和非线性函数来分散数据。引入了一个数学框架来分析它们的属性。不变量的计算涉及小波的多尺度收缩、层次对称性的线性化和稀疏分离。文中还讨论了相关应用。

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本文引用的文献

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