Goodness-of-fit tests based on the empirical Wasserstein distance are proposed for simple
and composite null hypotheses involving general multivariate distributions. For group …

Many recent superresolution methods are based on supervised learning. That means, that
they require a large database of pairs of high-and low-resolution images as training data …

Exploiting image patches instead of whole images has proved to be a powerful approach to
tackling various problems in image processing. Recently, Wasserstein patch priors (WPPs) …

When the images are formulated as discrete measures, computing Wasserstein-p distance
between them is challenging due to the complexity of solving the corresponding …

We propose GOTEX, a general framework for texture synthesis by optimization that
constrains the statistical distribution of local features. While our model encompasses several …

This work addresses texture synthesis by relying on the local representation of images
through their patch distributions. The main contribution is a framework that imposes the …

This paper investigates a new stochastic algorithm to approximate semi-discrete optimal
transport for large-scale problem, ie, in high dimension and for a large number of points. The …

The performance of algorithms for neural architecture search strongly depends on the
parametrization of the search space. We use contrastive learning to identify networks across …

Analyse de données géométriques, au delà des convolutionsPour modéliser des
interactions entre points, une méthode simple est de se reposer sur des sommes pondérées …

We propose to compute Wasserstein barycenters (WBs) by solving for Monge maps with
variational principle. We discuss the metric properties of WBs and explore their connections …