About me

I am a postdoctoral researcher in applied mathematics at the research center on computer science, signal processing and automatics of Lille (CRIStAL). My research interests include deep learning, generative models, computer vision and inverse problems. I have defended my PhD thesis on image restoration with deep generative models, under the supervision of Nicolas Papadakis and Andrés Almansa. I am currently working on combining deep learning and Bayesian methods for solving problems in cosmology.

News

• [02/07/2024] I will be presenting our work on inverse problem regularization with deep generative model at CAP-RFIAP 2024, on Tuesday 2nd of July.
• [23/04/2024] I will be presenting my work on inverse problem resolution with variational autoencoders at the 24th Congrès d analyse numérique (CANUM), on monday 27th of May.
• [10/12/2023] Our paper Efficient posterior sampling for diverse super-resolution with hierarchical VAE Prior has been accepted as an oral at the 19th International Joint Conference on Computer Vision Theory and Applications (VISAPP2024). preprint
• [15/11/2023] I have defended my PhD thesis Image restoration with deep generative models. The manuscript is available on HAL
• [14/07/2023] Our paper Inverse problem regularization with hierarchical variational autoencoders has been accepted as a poster in the main track of ICCV 2023!

Publications and preprints

• Un réseau de neurones augmenté pour simuler l'évolution de la distribution de matière noire
  Jean Prost, Pierre-Antoine Thouvenin, Jenny Sorce, Pierre Chainais
  (preprint) [arxiv]
• LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
  Alessio Spagnoletti, Jean Prost, Andrés Almansa, Nicolas Papadakis, Marcelo Pereyra
  (preprint) [arxiv]
• Efficient posterior sampling for diverse super-resolution with hierarchical VAE Prior
  Jean Prost, Antoine Houdard, Nicolas Papadakis, Andrés Almansa
  19th International Joint Conference on Computer Vision Theory and Applications (VISAPP2024) [arxiv]
• Plug-and-Play image restoration with Stochastic deNOising REgularization
  Marien Renaud, Jean Prost, Arthur Leclaire, Nicolas Papadakis
  Forty-first International Conference on Machine Learning [arxiv] [paper] [code]
• Inverse problem regularization with hierarchical variational autoencoders
  Jean Prost, Antoine Houdard, Nicolas Papadakis, Andrés Almansa
  International Conference on Computer Vision (ICCV 2023) [arxiv] [paper] [code]
• SCOTCH and SODA: A Transformer Video Shadow Detection Framework
  Lihao Liu, Jean Prost, Lei Zhu, Nicolas Papadakis, Pietro Lio, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
  Computer Vision and Pattern Recognition (CVPR 2023) [arxiv] [paper] [code]
• Learning Local Regularization for Variational Image Restoration
  Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis
  Scale Space and Variational Methods in Computer Vision: 8th International Conference, (SSVM 2021) [arxiv] [paper] [code]

Talks

• Mathematical Models for Plug-and-play Image Restoration
  08/12/2022, Paris
  Diverse image super-resolution with hierarchical variational autoencoders
• Cambridge Image Analysis seminar
  02/09/2022, Cambridge
  Diverse image super-resolution with hierarchical variational autoencoders
• Generative models: Control and (mis)Usage
  31/05/2022, CNRS Ile-de-France Villejuif
  Diverse image super-resolution with hierarchical variational autoencoders
• SIAM Conference on Imaging Science (IS22)
  22/03/2022, Virtual conference
  Learning local regularization for variational image restoration
• ORASIS 2021
  17/09/2021, Saint-Ferréol
  Apprentissage d'une fonction de régularisation locale pour la restauration d'images"