Understanding Concentration and Separation in Deep Neural Networks

12 November 2020
Stéphane Mallat

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Deep convolutional networks have spectacular performances that remain mostly not understood. Numerical experiments show that they classify by progressively concentrating each class in separate regions of a low-dimensional space. To explain these properties, we introduce a concentration and separation mechanism with multiscale tight frame contractions. Applications are shown for image classification and statistical physics models of cosmological structures and turbulent fluids.

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