  We introduce a two-layer wavelet scattering network, for object
classification. This scattering transform computes a spatial wavelet transform
on the first layer and a new joint wavelet transform along spatial, angular and
scale variables in the second layer. Numerical experiments demonstrate that
this two layer convolution network, which involves no learning and no max
pooling, performs efficiently on complex image data sets such as CalTech, with
structural objects variability and clutter. It opens the possibility to
simplify deep neural network learning by initializing the first layers with
wavelet filters.
