2 code implementations • 18 Dec 2020 • John Zarka, Florentin Guth, Stéphane Mallat
On the opposite, a soft-thresholding on tight frames can reduce within-class variabilities while preserving class means.
1 code implementation • ICLR 2022 • Florentin Guth, John Zarka, Stéphane Mallat
Spatial variability is therefore transformed into variability along channels.
1 code implementation • 4 Oct 2023 • Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli, Stéphane Mallat
Finally, we show that when trained on regular image classes for which the optimal basis is known to be geometry-adaptive and harmonic, the denoising performance of the networks is near-optimal.
1 code implementation • 6 Mar 2023 • Zahra Kadkhodaie, Florentin Guth, Stéphane Mallat, Eero P Simoncelli
We instantiate this model using convolutional neural networks (CNNs) with local receptive fields, which enforce both the stationarity and Markov properties.
1 code implementation • 31 May 2023 • Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat
There is a growing gap between the impressive results of deep image generative models and classical algorithms that offer theoretical guarantees.
no code implementations • ICLR 2021 • John Zarka, Florentin Guth, Stéphane Mallat
Numerical experiments demonstrate that deep neural networks classifiers progressively separate class distributions around their mean, achieving linear separability.
no code implementations • 9 Aug 2022 • Florentin Guth, Simon Coste, Valentin De Bortoli, Stephane Mallat
This is because of ill-conditioning properties of the score that we analyze mathematically.
no code implementations • 29 May 2023 • Florentin Guth, Brice Ménard, Gaspar Rochette, Stéphane Mallat
Gaussian rainbow networks are defined with Gaussian weight distributions.