
Learning local regularization for variational image restoration
In this work, we propose a framework to learn a local regularization mod...
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On the Existence of Optimal Transport Gradient for Learning Generative Models
The use of optimal transport cost for learning generative models has bec...
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GraphXCOVID: Explainable Deep Graph Diffusion PseudoLabelling for Identifying COVID19 on Chest Xrays
Can one learn to diagnose COVID19 under extreme minimal supervision? Si...
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Multitask deep learning for image segmentation using recursive approximation tasks
Fully supervised deep neural networks for segmentation usually require a...
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Variational Osmosis for Nonlinear Image Fusion
We propose a new variational model for nonlinear image fusion. Our appro...
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GraphX^NET Chest XRay Classification Under Extreme Minimal Supervision
The task of classifying Xray data is a problem of both theoretical and ...
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A multitask Unet for segmentation with lazy labels
The need for labour intensive pixelwise annotation is a major limitatio...
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Beyond Supervised Classification: Extreme Minimal Supervision with the Graph 1Laplacian
We consider the task of classifying when an extremely reduced amount of ...
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Robust superpixels using color and contour features along linear path
Superpixel decomposition methods are widely used in computer vision and ...
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SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Superpixels have become very popular in many computer vision application...
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An Optimized PatchMatch for Multiscale and Multifeature Label Fusion
Automatic segmentation methods are important tools for quantitative anal...
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Evaluation Framework of Superpixel Methods with a Global Regularity Measure
In the superpixel literature, the comparison of stateoftheart methods...
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SCALP: Superpixels with Contour Adherence using Linear Path
Superpixel decomposition methods are generally used as a preprocessing ...
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Robust Shape Regularity Criteria for Superpixel Evaluation
Regular decompositions are necessary for most superpixelbased object re...
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Superpixelbased Color Transfer
In this work, we propose a fast superpixelbased color transfer method (...
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TextureAware Superpixel Segmentation
Most superpixel methods are based on spatial and color measures at the p...
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Approximation of Wasserstein distance with Transshipment
An algorithm for approximating the pWasserstein distance between histog...
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Datadriven regularization of Wasserstein barycenters with an application to multivariate density registration
We present a framework to simultaneously align and smooth data in the fo...
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Central limit theorems for Sinkhorn divergence between probability distributions on finite spaces and statistical applications
The notion of Sinkhorn divergence has recently gained popularity in mach...
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Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering
In this short paper, we formulate parameter estimation for finite mixtur...
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Regularized Optimal Transport and the Rot Mover's Distance
This paper presents a unified framework for smooth convex regularization...
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Convex HistogramBased Joint Image Segmentation with Regularized Optimal Transport Cost
We investigate in this work a versatile convex framework for multiple im...
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Convex Color Image Segmentation with Optimal Transport Distances
This work is about the use of regularized optimaltransport distances fo...
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Regularized Discrete Optimal Transport
This article introduces a generalization of the discrete optimal transpo...
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Nicolas Papadakis
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