HOUDARD, Antoine; LECLAIRE, Arthur; PAPADAKIS, Nicolas; RABIN, Julien

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HOUDARD, Antoine
Institut de Mathématiques de Bordeaux [IMB]

LECLAIRE, Arthur
Institut de Mathématiques de Bordeaux [IMB]

PAPADAKIS, Nicolas
Institut de Mathématiques de Bordeaux [IMB]

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Ce document a été publié dans

Transactions on Machine Learning Research Journal. 2023-07-14

[Amherst Massachusetts]: OpenReview.net, 2022

Résumé en anglais

The use of optimal transport costs for learning generative models has become popular with Wasserstein Generative Adversarial Networks (WGANs). Training a WGAN requires the computation of the differentiation of the optimal transport cost with respect to the parameters of the generative model. In this work, we provide sufficient conditions for the existence of a gradient formula in two different frameworks: the case of semi-discrete optimal transport (i.e. with a discrete target distribution) and the case of regularized optimal transport (i.e. with an entropic penalty). Both cases are based on the dual formulation of the transport cost, and the gradient formula involves a solution of the dual problem. The learning problem is addressed with an alternate algorithm, whose behavior is examined for the problem of MNIST digits generation. In particular, we analyze the impact of entropic regularization both on visual results and convergence speed.< Réduire

URI

https://oskar-bordeaux.fr/handle/20.500.12278/191093

Project ANR

Models, Inference and Synthesis for Texture In Color - ANR-19-CE40-0005
Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010

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Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251