  This paper introduces a Deep Scattering network that utilizes Dual-Tree
complex wavelets to extract translation invariant representations from an input
signal. The computationally efficient Dual-Tree wavelets decompose the input
signal into densely spaced representations over scales. Translation invariance
is introduced in the representations by applying a non-linearity over a region
followed by averaging. The discriminatory information in the densely spaced,
locally smooth, signal representations aids the learning of the classifier. The
proposed network is shown to outperform Mallat's ScatterNet on four datasets
with different modalities on classification accuracy.
