activity
20182021
most citedEnd-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On

19 citations · 19 across the 1 of their papers we have counts for

collaborators

5 papers

cs.LG2021

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…

cs.CV2020

Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On

Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes

The 2D virtual try-on task has recently attracted a great interest from the research community, for its direct potential applications in online shopping as well as for its inherent…

stat.ML2020

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…

cs.CV201919 cited

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…

cs.CV2018

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…