On the Gradient Formula for learning Generative Models with Regularized Optimal Transport Costs
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | HOUDARD, Antoine | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | LECLAIRE, Arthur | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | PAPADAKIS, Nicolas | |
| hal.structure.identifier | École Nationale Supérieure d'Ingénieurs de Caen [ENSICAEN] | |
| dc.contributor.author | RABIN, Julien | |
| dc.date.accessioned | 2024-04-04T02:40:48Z | |
| dc.date.available | 2024-04-04T02:40:48Z | |
| dc.date.created | 2022 | |
| dc.date.issued | 2023-07-14 | |
| dc.identifier.issn | 2835-8856 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191093 | |
| dc.description.abstractEn | 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. | |
| dc.description.sponsorship | Models, Inference and Synthesis for Texture In Color - ANR-19-CE40-0005 | |
| dc.description.sponsorship | Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010 | |
| dc.language.iso | en | |
| dc.publisher | [Amherst Massachusetts]: OpenReview.net, 2022 | |
| dc.title.en | On the Gradient Formula for learning Generative Models with Regularized Optimal Transport Costs | |
| dc.type | Article de revue | |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
| dc.subject.hal | Statistiques [stat]/Machine Learning [stat.ML] | |
| bordeaux.journal | Transactions on Machine Learning Research Journal | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-03740368 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03740368v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Transactions%20on%20Machine%20Learning%20Research%20Journal&rft.date=2023-07-14&rft.eissn=2835-8856&rft.issn=2835-8856&rft.au=HOUDARD,%20Antoine&LECLAIRE,%20Arthur&PAPADAKIS,%20Nicolas&RABIN,%20Julien&rft.genre=article |
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