19 citations · 41 across the 7 of their papers we have counts for
4 papers · 1 filter
Diffusion Models for Counterfactual Explanations
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable. In this paper, we propose DiME, a method allowing the g…
Training face verification models from generated face identity data
Dennis Conway, Loic Simon, Alexis Lechervy +1
Machine learning tools are becoming increasingly powerful and widely used. Unfortunately membership attacks, which seek to uncover information from data sets used in machine learni…
This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces
Ryan Webster, Julien Rabin, Loic Simon +1
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website {\small \url{ http://this…
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
Mateusz Koziński, Loïc Simon, Frédéric Jurie
We propose a method for semi-supervised training of structured-output neural networks. Inspired by the framework of Generative Adversarial Networks (GAN), we train a discriminator…