G

Generative Adversarial Networks

General style transfer framework that contains a set of popular GANs for image style transfer using unpaired data samples.

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Stage de fin d'étude sur le développement d'un modèle d'apprentissage par renforcement multi-objectifs pour répondre à des problèmes éthiques.

Encadrants : Aurélie Beynier, Nicolas Maudet, Paolo Viappiani Date : Mars - Septembre 2022

PyTorch realization of some GANS: fully connected GAN, convolutional GAN, GAN with Wasserstein loss (including gradient penalty version).