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Alae[CVPR2020] Adversarial Latent Autoencoders
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DLSSDeep Learning Super Sampling with Deep Convolutional Generative Adversarial Networks.
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Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
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Mimicry[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
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RganRecurrent (conditional) generative adversarial networks for generating real-valued time series data.
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Pytorch Mnist Celeba Cgan CdcganPytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
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Gans In ActionCompanion repository to GANs in Action: Deep learning with Generative Adversarial Networks
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TpudcganTrain DCGAN with TPUs on Google Cloud
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Anime ganGAN models with Anime.
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Pix2pixImage-to-image translation with conditional adversarial nets
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