Generic Deep Networks with Wavelet Scattering

By Edouard Oyallon, Stéphane Mallat, Laurent Sifre. 2013

54 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Spectral Representations for Convolutional Neural Networks (2015, methodology): “While wavelets have been employed throughout machine learning with great promise (Bruna & Mallat, 2013; Oyallon et al., 2013), to our knowledge they have not been used in an adaptive way to learn CNNs.”
Rigid-Motion Scattering for Texture Classification (2014, background): “Recent work (Oyallon et al., 2014) has shown that rigid-motion scattering, with extension to dilation, could also be used for more generic vision task such as object recognition, with promising re-sults on the CalTech 101 and 256 datasets.”
Roto-Translation Covariant Convolutional Networks for Medical Image Analysis (2018, background): “In work by Mallat, Oyallon, and Sifre [5, 4] roto-translation invariant deep networks are formulated in the context of scattering theory.”