Rnn.wganCode for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"
Gan TutorialSimple Implementation of many GAN models with PyTorch.
Dcgan wgan wgan Gp lsgan sngan rsgan began acgan pggan tensorflowImplementation of some different variants of GANs by tensorflow, Train the GAN in Google Cloud Colab, DCGAN, WGAN, WGAN-GP, LSGAN, SNGAN, RSGAN, RaSGAN, BEGAN, ACGAN, PGGAN, pix2pix, BigGAN
Gan theoriesResources and Implementations of Generative Adversarial Nets which are focusing on how to stabilize training process and generate high quality images: DCGAN, WGAN, EBGAN, BEGAN, etc.
Pytorch GanA minimal implementaion (less than 150 lines of code with visualization) of DCGAN/WGAN in PyTorch with jupyter notebooks
Tf Exercise GanTensorflow implementation of different GANs and their comparisions
Ganotebookswgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch
Awesome GansAwesome Generative Adversarial Networks with tensorflow
Tf.gans ComparisonImplementations of (theoretical) generative adversarial networks and comparison without cherry-picking
Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
GAN-Anime-CharactersApplied several Generative Adversarial Networks (GAN) techniques such as: DCGAN, WGAN and StyleGAN to generate Anime Faces and Handwritten Digits.
WGAN-GP-tensorflowTensorflow Implementation of Paper "Improved Training of Wasserstein GANs"
Generative-ModelRepository for implementation of generative models with Tensorflow 1.x
coursera-gan-specializationProgramming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Pytorch-Basic-GANsSimple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
ganGenerate new images with Generative Adversarial Network and Tensorflow.
Fun-with-MNISTPlaying with MNIST. Machine Learning. Generative Models.