Collection of generative models in Tensorflow

Collection of generative models in Pytorch version.

wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

Awesome Generative Adversarial Networks with tensorflow

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

Reimplementation of GANs

Simple Implementation of many GAN models with PyTorch.

Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"

Implementation 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

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

A minimal implementaion (less than 150 lines of code with visualization) of DCGAN/WGAN in PyTorch with jupyter notebooks

Resources 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.

GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.

implementation of several GANs with pytorch

A Tensorflow implementation of GAN, WGAN and WGAN with gradient penalty.

Tensorflow implementation of different GANs and their comparisions

Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.

🚀 Variants of GANs most easily implemented as TensorFlow2. GAN, DCGAN, LSGAN, WGAN, WGAN-GP, DRAGAN, ETC...