ScatNet: An implementation of Scattering Networks transforms and classification algorithms

Triggered by Sebastien Bubeck blog entry posted on the Google+ group, I got to find out that Mallat et al 's scattering transform is now hosted under a toolbox called ScatNet. From the page: 

ScatNet is a MATLAB implementation of Scattering Networks transforms and classification algorithms, with reproduction of experiments and figures from papers. It includes:

• Scattering of one-dimensional signals [1] (figures reproduced)

• Time scattering of audio signals [3] (figures reproduced)

• Frequency Scattering for audio [3] (figures reproduced)

• Scattering for images [2, 4]

• Roto-translation scattering for images [4]

References :

[1] Mathematical introduction of scattering operators for translation and rotation invariant representations, S. Mallat, "Group Invariant Scattering" Communications in Pure and Applied Mathematics, October 2012.

[2] Scattering transform for image classification, J.Bruna and S. Mallat, IEEE Trans. on PAMI, August 2013 : "Invariant Scattering Convolution Network"

[3] Scattering for audio signals, J. Anden, S. Mallat "Deep Scattering Spectrum" , http://arxiv.org/abs/1304.6763, submitted to IEEE trans on signal processing.

[4] Rotation invariant scattering for images, L. Sifre and S. Mallat, "Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination", in Proc. IEEE CVPR 2013 conference.

If you recall the idea is to first nonlinearly transform a signal so that translated'dilated/rotated version of the same signal produce the same result of said signal thereby allowing for the euclidian norm to be a good measure of neighborliness.

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