1 code implementation • 17 Mar 2023 • Ryan Webster, Julien Rabin, Loic Simon, Frederic Jurie
Generative models, such as DALL-E, Midjourney, and Stable Diffusion, have societal implications that extend beyond the field of computer science.
no code implementations • 13 Jul 2021 • Ryan Webster, Julien Rabin, Loic Simon, Frederic Jurie
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers.
1 code implementation • 30 Mar 2021 • Amitoz Azad, Julien Rabin, Abderrahim Elmoataz
In this work, we investigate the use of variational models for such GNN to process signals on graphs for unsupervised learning.
no code implementations • 10 Feb 2021 • Antoine Houdard, Arthur Leclaire, Nicolas Papadakis, Julien Rabin
Training of WGAN relies on a theoretical background: the calculation of the gradient of the optimal transport cost with respect to the generative model parameters.
no code implementations • 21 Jun 2020 • Rodrigue Siry, Ryan Webster, Loic Simon, Julien Rabin
The recent advent of powerful generative models has triggered the renewed development of quantitative measures to assess the proximity of two probability distributions.
1 code implementation • 19 Jun 2020 • Antoine Houdard, Arthur Leclaire, Nicolas Papadakis, Julien Rabin
The GOTEX model based on patch features is also adapted to texture inpainting and texture interpolation.
1 code implementation • 13 Jan 2020 • Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut
Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that reproduce the visual characteristics of the examples along some directions.
1 code implementation • 14 May 2019 • Loïc Simon, Ryan Webster, Julien Rabin
In this article we revisit the definition of Precision-Recall (PR) curves for generative models proposed by Sajjadi et al. (arXiv:1806. 00035).
1 code implementation • CVPR 2019 • Ryan Webster, Julien Rabin, Loic Simon, Frederic Jurie
Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods.
no code implementations • 14 Nov 2016 • Ronan Sicre, Julien Rabin, Yannis Avrithis, Teddy Furon, Frederic Jurie
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built.
no code implementations • 5 Oct 2016 • Nicolas Papadakis, Julien Rabin
We investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory.
no code implementations • 6 Mar 2015 • Julien Rabin, Nicolas Papadakis
This work is about the use of regularized optimal-transport distances for convex, histogram-based image segmentation.