298 citations · 366 across the 5 of their papers we have counts for
11 papers
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge
Eustache Diemert, Romain Fabre, Alexandre Gilotte +6
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed und…
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…
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…
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…
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…
Distributionally Robust Reinforcement Learning
Elena Smirnova, Elvis Dohmatob, Jérémie Mary
Real-world applications require RL algorithms to act safely. During learning process, it is likely that the agent executes sub-optimal actions that may lead to unsafe/poor states o…