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Renard, Philippe
Nom
Renard, Philippe
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Directeur de Recherche
Email
Philippe.Renard@unine.ch
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- PublicationAccès libre3D Geological Image Synthesis from 2D Examples Using Generative Adversarial Networks(2020-10)
; Lefebvre, SylvainGenerative Adversarial Networks (GAN) are becoming an alternative to Multiple-point Statistics (MPS) techniques to generate stochastic fields from training images. But a difficulty for all the training image based techniques (including GAN and MPS) is to generate 3D fields when only 2D training data sets are available. In this paper, we introduce a novel approach called Dimension Augmenter GAN (DiAGAN) enabling GANs to generate 3D fields from 2D examples. The method is simple to implement and is based on the introduction of a random cut sampling step between the generator and the discriminator of a standard GAN. Numerical experiments show that the proposed approach provides an efficient solution to this long lasting problem.