title: Invariant scattering convolution networks.
abstract: A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.
authors.affiliation: Courant Institute, New York University, 715 Broadway, New York, NY 10003, USA. joan.bruna@gmail.com
authors.forename: Joan
authors.initials: J
authors.lastname: Bruna
authors.forename: Stéphane
authors.initials: S
authors.lastname: Mallat