BARLAUD , Michel

Nous proposons dans ce papier d'étudier les métriques de similarité entre images dans le cadre des problèmes inverses tels que la déconvolution et la séparation de sources. La métrique de similarité que nous proposons est basée sur les notions de théorie de l'information (distance de Kullback-Leibler) et est combinée avec une transformée en...

This paper deals with supervised discriminative and generative modeling. Classical methods are based on variational autoencoders or supervised variational autoencoders encourage the latent space to fit a prior distribution, like a Gaussian. However, they tend to make stronger assumptions on the data, often leading to higher asymptotic bias when...

International audience

This paper deals with supervised classification and feature selection with application in the context of high dimensional features. A classical approach leads to an optimization problem minimizing the within sum of squares in the clusters (2 norm) with an 1 penalty in order to promote sparsity. It has been known for decades that 1 norm is more...

Deep neural networks (DNN) have been applied recently to different domains andperform better than classical state-of-the-art methods. However the high level of performances of DNNs is most often obtained with networks containing millions of parameters and for which training requires substantial computational power. To deal with this...

International audience

In this paper we address the image restoration problem in the variational framework. Classical approaches minimize the Lp norm of the residual and rely on parametric assumptions on the noise statistical model. We relax this parametric hypothesis and we formulate the problem on the basis of nonparametric density estimates. The proposed approach...

The recent improvements of graphics processing units (GPU) offer to the computer vision community a powerful processing platform. Indeed, a lot of highly-parallelizable computer vision problems can be significantly accelerated using GPU architecture. Among these algorithms, the k nearest neighbor search (KNN) is a well-known problem linked with...

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Nearest Neighbor (NN) retrieval is a crucial tool of many computer vision tasks. Since the brute-force naive search is too time consuming for most applications, several tailored data structures have been proposed to improve the efficiency of NN search. Among these, vantage point tree (vp-tree) was introduced for information retrieval in metric...

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