708 citations · 2.8k across the 48 of their papers we have counts for
10 papers · 1 filter
Learning multi-scale local conditional probability models of images
Zahra Kadkhodaie, Florentin Guth, Stéphane Mallat +1
Deep neural networks can learn powerful prior probability models for images, as evidenced by the high-quality generations obtained with recent score-based diffusion methods. But th…
Generalized Rectifier Wavelet Covariance Models For Texture Synthesis
Antoine Brochard, Sixin Zhang, Stéphane Mallat
State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN). Such repres…
Deep Roto-Translation Scattering for Object Classification
Edouard Oyallon, Stéphane Mallat
Dictionary learning algorithms or supervised deep convolution networks have considerably improved the efficiency of predefined feature representations such as SIFT. We introduce a…
Rigid-Motion Scattering for Texture Classification
Laurent SIfre, Stéphane Mallat
A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network. Convolutions are calculated on the rigid-motion group, w…
Generic Deep Networks with Wavelet Scattering
Edouard Oyallon, Stéphane Mallat, Laurent Sifre
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 wa…
Invariant Scattering Convolution Networks
Joan Bruna, Stéphane Mallat
A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification. It…
Classification with Invariant Scattering Representations
Joan Bruna, Stéphane Mallat
A scattering transform defines a signal representation which is invariant to translations and Lipschitz continuous relatively to deformations. It is implemented with a non-linear c…
Geometric Models with Co-occurrence Groups
Joan Bruna, Stéphane Mallat
A geometric model of sparse signal representations is introduced for classes of signals. It is computed by optimizing co-occurrence groups with a maximum likelihood estimate calcul…
Classification with Scattering Operators
Joan Bruna, Stéphane Mallat
A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information. It is computed with a cascade of wavelet decompositions and complex mo…
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
Guoshen Yu, Guillermo Sapiro, Stéphane Mallat
A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-…