Grogan et al. [11, 12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour distributions of two images (palette and target). It was shown to be very competitive to alternative solutions based on Optimal Transport for colour transfer. We show that in fact Grogan et al’s formulation can also be understood as a new robust Optimal Transport based framework with entropy regularisation over marginals
The distance that compares the difference between two probability distributions plays a fundamental ...
International audienceClassical optimal transport problem seeks a transportation map that preserves ...
This paper introduces a new class of algorithms for optimization problems involving optimal transpor...
Grogan et al. [11, 12] have recently proposed a solution to colour transfer by minimising the Euclid...
Optimal Transport (OT) is a very popular framework for performing colour transfer in images and vide...
International audienceThis paper introduces a new class of algorithms for optimization problems invo...
Regularised optimal transport theory has been gaining increasing interest in machine learning as a v...
Comparing and matching probability distributions is a crucial in numerous machine learning (ML) algo...
Optimal Transport is a well developed mathematical theory that defines robust metrics between probab...
This article introduces a generalization of the discrete optimal transport, with applications to col...
The Optimal Transport theory not only defines a notion of distance between probability measures, but...
We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal tr...
54 pages, 12 figuresThis paper is focused on the study of entropic regularization in optimal transpo...
Comparing probability distributions is a fundamental problem in data sciences. Simple norms and dive...
Over the past few years, optimal transport has gained popularity in machine learning as a way to com...
The distance that compares the difference between two probability distributions plays a fundamental ...
International audienceClassical optimal transport problem seeks a transportation map that preserves ...
This paper introduces a new class of algorithms for optimization problems involving optimal transpor...
Grogan et al. [11, 12] have recently proposed a solution to colour transfer by minimising the Euclid...
Optimal Transport (OT) is a very popular framework for performing colour transfer in images and vide...
International audienceThis paper introduces a new class of algorithms for optimization problems invo...
Regularised optimal transport theory has been gaining increasing interest in machine learning as a v...
Comparing and matching probability distributions is a crucial in numerous machine learning (ML) algo...
Optimal Transport is a well developed mathematical theory that defines robust metrics between probab...
This article introduces a generalization of the discrete optimal transport, with applications to col...
The Optimal Transport theory not only defines a notion of distance between probability measures, but...
We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal tr...
54 pages, 12 figuresThis paper is focused on the study of entropic regularization in optimal transpo...
Comparing probability distributions is a fundamental problem in data sciences. Simple norms and dive...
Over the past few years, optimal transport has gained popularity in machine learning as a way to com...
The distance that compares the difference between two probability distributions plays a fundamental ...
International audienceClassical optimal transport problem seeks a transportation map that preserves ...
This paper introduces a new class of algorithms for optimization problems involving optimal transpor...