Unsupervised Learning by Deep Scattering Contractions

We introduce a deep scattering network, which computes invariants with iterated contractions adapted to training data. It defines a deep convolution network model, whose contraction properties can be analyzed mathematically. A cascade of wavelet transform convolutions are computed with a multirate filter bank, and adapted with permutations. Unsupervised learning of permutations optimize the contraction directions, by maximizing the average discriminability of training data. For Haar wavelets, it is solved with a polynomial complexity pairing algorithm. Translation and rotation invariance learning is shown with classification experiments on hand-written digits.