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Continuous Methods : Hamiltonian Domain Translation

Emmanuel Menier 1, 2, 3 Michele Alessandro Bucci 2 Mouadh Yagoubi 3 Lionel Mathelin 1, 4 Marc Schoenauer 2, 1, 5 
4 DATAFLOT - DAtascience, trAnsition, Fluid instability, contrOl, Turbulence
LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, M.-E. - Mécanique-Energétique
5 A&O - A&O (Apprentissage et Optimisation)
LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, SDD - Science des Données, AAC - Algorithmes, Apprentissage et Calcul
Abstract : This paper proposes a novel approach to domain translation. Leveraging established parallels between generative models and dynamical systems, we propose a reformulation of the Cycle-GAN architecture. By embedding our model with a Hamiltonian structure, we obtain a continuous, expressive and most importantly invertible generative model for domain translation.
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Preprints, Working Papers, ...
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Contributor : Emmanuel Menier Connect in order to contact the contributor
Submitted on : Thursday, July 7, 2022 - 4:05:03 PM
Last modification on : Tuesday, October 18, 2022 - 8:34:05 AM


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  • HAL Id : hal-03716629, version 1


Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi, Lionel Mathelin, Marc Schoenauer. Continuous Methods : Hamiltonian Domain Translation. 2022. ⟨hal-03716629⟩



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