Publications (10)
Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach
Thibaut Issenhuth, Vinkle Srivastav, Afshin Gangi +1
Purpose: Face detection is a needed component for the automatic analysis and assistance of human activities during surgical procedures. Efficient face detection algorithms can inde…
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…
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…
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…
End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes
The 2D virtual try-on task has recently attracted a lot of interest from the research community, for its direct potential applications in online shopping as well as for its inheren…
Learning disconnected manifolds: a no GANs land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob +1
Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator. Consequently, the modeled distribution alw…