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Invariant Scattering Convolution Networks

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2 Author(s)
Bruna, J. ; Courant Inst., New York Univ., New York, NY, USA ; Mallat, S.

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.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:35 ,  Issue: 8 )