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A scattering transform defines a signal representation which is invariant totranslations and Lipschitz continuous relatively to deformations. It isimplemented with a non-linear convolution network that iterates over waveletand modulus operators. Lipschitz continuity locally linearizes deformations.Complex classes of signals and textures can be modeled with low-dimensionalaffine spaces, computed with a PCA in the scattering domain. Classification isperformed with a penalized model selection. State of the art results areobtained for handwritten digit recognition over small training sets, and fortexture classification.

Classification with Invariant Scattering Representations