19 citations · 57 across the 9 of their papers we have counts for
5 papers · 1 filter
Position: Fairness Failure in Generative Models is an Evaluation Problem
Mariia Vladimirova, Jean-Yves Franceschi, Thibaut Issenhuth
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized g…
Improving Consistency Models with Generator-Augmented Flows
Thibaut Issenhuth, Sangchul Lee, Ludovic Dos Santos +3
Consistency models imitate the multi-step sampling of score-based diffusion in a single forward pass of a neural network. They can be learned in two ways: consistency distillation…
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos +4
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle…
Unveiling the Latent Space Geometry of Push-Forward Generative Models
Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary +1
Many deep generative models are defined as a push-forward of a Gaussian measure by a continuous generator, such as Generative Adversarial Networks (GANs) or Variational Auto-Encode…
Latent reweighting, an almost free improvement for GANs
Thibaut Issenhuth, Ugo Tanielian, David Picard +1
Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting different classes of images. In particu…