  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
non-linear modulus and averaging operators. The first network layer outputs
SIFT-type descriptors whereas the next layers provide complementary invariant
information which 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, using a Gaussian kernel SVM and a generative
PCA classifier.
