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Pré-Publication, Document De Travail Année : 2023

Unifying GANs and Score-Based Diffusion as Generative Particle Models

Mike Gartrell
  • Fonction : Auteur
Ludovic Dos Santos
Emmanuel de Bézenac
  • Fonction : Auteur
  • PersonId : 1101543
  • IdRef : 259426148
Mickaël Chen
  • Fonction : Auteur
  • PersonId : 1101545
Alain Rakotomamonjy
  • Fonction : Auteur
  • PersonId : 1102415

Résumé

Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions by differential equations is conventionally seen as opposed to the previously widespread generative adversarial networks (GANs), which involve training a pushforward generator network. In this paper, we challenge this interpretation and propose a novel framework that unifies particle and adversarial generative models by framing generator training as a generalization of particle models. This suggests that a generator is an optional addition to any such generative model. Consequently, integrating a generator into a score-based diffusion model and training a GAN without a generator naturally emerge from our framework. We empirically test the viability of these original models as proofs of concepts of potential applications of our framework.
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Licence : CC BY - Paternité

Dates et versions

hal-04107806 , version 1 (26-05-2023)
hal-04107806 , version 2 (26-10-2023)
hal-04107806 , version 3 (21-12-2023)

Identifiants

Citer

Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth, Emmanuel de Bézenac, et al.. Unifying GANs and Score-Based Diffusion as Generative Particle Models. 2023. ⟨hal-04107806v1⟩
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