4 citations · 5 across the 4 of their papers we have counts for
6 papers
Evolutionary Retrofitting
Mathurin Videau, Mariia Zameshina, Alessandro Leite +3
AfterLearnER (After Learning Evolutionary Retrofitting) consists in applying evolutionary optimization to refine fully trained machine learning models by optimizing a set of carefu…
Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
Ismail Labiad, Thomas Bäck, Pierre Fernandez +5
In many cases, adversarial attacks are based on specialized algorithms specifically dedicated to attacking automatic image classifiers. These algorithms perform well, thanks to an…
PrivacyGAN: robust generative image privacy
Mariia Zameshina, Marlene Careil, Olivier Teytaud +1
Classical techniques for protecting facial image privacy typically fall into two categories: data-poisoning methods, exemplified by Fawkes, which introduce subtle perturbations to…
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
Mariia Zameshina, Olivier Teytaud, Laurent Najman
Latent diffusion models excel at producing high-quality images from text. Yet, concerns appear about the lack of diversity in the generated imagery. To tackle this, we introduce Di…
Fairness in generative modeling
Mariia Zameshina, Olivier Teytaud, Fabien Teytaud +4
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling. More precisely, to design fair algorithms for as many sensitive variab…
EvolGAN: Evolutionary Generative Adversarial Networks
Baptiste Roziere, Fabien Teytaud, Vlad Hosu +4
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new…