1 code implementation • 25 May 2023 • Louis Bethune, Thomas Massena, Thibaut Boissin, Yannick Prudent, Corentin Friedrich, Franck Mamalet, Aurelien Bellet, Mathieu Serrurier, David Vigouroux
To provide sensitivity bounds and bypass the drawbacks of the clipping process, we propose to rely on Lipschitz constrained networks.
1 code implementation • CVPR 2023 • Thomas Fel, Agustin Picard, Louis Bethune, Thibaut Boissin, David Vigouroux, Julien Colin, Rémi Cadène, Thomas Serre
However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image -- revealing "where" the model looks, but failing to elucidate "what" the model sees in those areas.
no code implementations • 13 Oct 2022 • Thomas Mullor, David Vigouroux, Louis Bethune
Quantum walks on binary trees are used in many quantum algorithms to achieve important speedup over classical algorithms.
1 code implementation • 13 Jun 2022 • Paul Novello, Thomas Fel, David Vigouroux
HSIC measures the dependence between regions of an input image and the output of a model based on kernel embeddings of distributions.
1 code implementation • 9 Jun 2022 • Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin Plakoo, Remi Cadene, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis Bethune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Gregory Flandin, Thomas Serre
Today's most advanced machine-learning models are hardly scrutable.
no code implementations • CVPR 2023 • Thomas Fel, Melanie Ducoffe, David Vigouroux, Remi Cadene, Mikael Capelle, Claire Nicodeme, Thomas Serre
A variety of methods have been proposed to try to explain how deep neural networks make their decisions.
1 code implementation • NeurIPS 2021 • Thomas Fel, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre
We describe a novel attribution method which is grounded in Sensitivity Analysis and uses Sobol indices.
no code implementations • 7 Sep 2020 • Thomas Fel, David Vigouroux, Rémi Cadène, Thomas Serre
A plethora of methods have been proposed to explain how deep neural networks reach their decisions but comparatively, little effort has been made to ensure that the explanations produced by these methods are objectively relevant.
no code implementations • 5 Nov 2018 • David Vigouroux, Sylvain Picard
We propose a method to obtain robust features in unsupervised learning tasks against adversarial attacks.