54 citations · 57 across the 5 of their papers we have counts for
6 papers
From CNNs to Shift-Invariant Twin Models Based on Complex Wavelets
Hubert Leterme, Kévin Polisano, Valérie Perrier +1
We propose a novel method to increase shift invariance and prediction accuracy in convolutional neural networks. Specifically, we replace the first-layer combination "real-valued c…
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks
Hubert Leterme, Kévin Polisano, Valérie Perrier +1
This paper focuses on improving the mathematical interpretability of convolutional neural networks (CNNs) in the context of image classification. Specifically, we tackle the instab…
Riesz-based orientation of localizable Gaussian fields
Kévin Polisano, Marianne Clausel, Valérie Perrier +1
In this work we give a sense to the notion of orientation for self-similar Gaussian fields with stationary increments, based on a Riesz analysis of these fields, with isotropic zer…
Texture Modeling by Gaussian fields with prescribed local orientation
Kévin Polisano, Marianne Clausel, Valérie Perrier +1
This paper presents a new framework for oriented texture modeling. We introduce a new class of Gaussian fields, called Locally Anisotropic Fractional Brownian Fields, with prescrib…
The Monogenic Synchrosqueezed Wavelet Transform: A tool for the Decomposition/Demodulation of AM-FM images
Marianne Clausel, Thomas Oberlin, Valérie Perrier
The synchrosqueezing method aims at decomposing 1D functions as superpositions of a small number of "Intrinsic Modes", supposed to be well separated both in time and frequency. Bas…
Divergence-free Wavelets for Navier-Stokes
Erwan Deriaz, Valérie Perrier
In this paper, we investigate the use of compactly supported divergence-free wavelets for the representation of the Navier-Stokes solution. After reminding the theoretical construc…