Thibaut Issenhuth
I am a researcher in deep generative models at Criteo AI Lab. I completed my PhD in 2023 in collaboration with Criteo AI Lab and Imagine Lab, Ecole des Ponts ParisTech. I was advised by Jérémie Mary (Criteo) and David Picard (ENPC).
Research interest. My research focus on the use of deep generative models on high-dimensional data, particularly on natural images. I am interested in both the theoretical understanding and the potential applications of these models. My main contributions concern improving and understanding Generative Adversarial Networks (GANs) on multi-modal and disconnected data distributions, and deep neural networks for image editing.
selected publications
- NeurIPSUnifying GANs and Score-Based Diffusion as Generative Particle ModelsIn Conference on Neural Information Processing Systems 2023
- ICMLUnveiling the latent space geometry of push-forward generative modelsIn International Conference on Machine Learning, 2023
- TMLR
- WACVLatent reweighting, an almost free improvement for GANsIn Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022
- ICMLLearning disconnected manifolds: a no gan’s landIn International Conference on Machine Learning, 2020
- ECCVDo not mask what you do not need to mask: a parser-free virtual try-onIn European Conference on Computer Vision, 2020
- IJCARSFace detection in the operating room: Comparison of state-of-the-art methods and a self-supervised approachInternational journal of computer assisted radiology and surgery, 2019