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Invariant Scattering Convolution Networks
Aug. 2013 (vol. 35 no. 8)
pp. 1872-1886
Joan Bruna, Ecole Polytechnique, Palaiseau
Stephane Mallat, Ecole Normale Suprieure, Paris
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.
Index Terms:
wavelets,Classification,convolution networks,deformations,invariants
Citation:
Joan Bruna, Stephane Mallat, "Invariant Scattering Convolution Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1872-1886, Aug. 2013, doi:10.1109/TPAMI.2012.230
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