Collection of generative models in Tensorflow

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

Collection of generative models in Pytorch version.

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

learn code with tensorflow

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

Simple Implementation of many GAN models with PyTorch.

PyTorch Implementation of InfoGAN

Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.

MATLAB implementations of Generative Adversarial Networks -- from GAN to Pixel2Pixel, CycleGAN

:dolls: InfoGAN: Interpretable Representation Learning

implement infoGAN using pytorch

InfoGAN inspired neural network trained on zap50k images (using Tensorflow + tf-slim). Intermediate layers of the discriminator network are used to do image similarity.

Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE

Repository for implementation of generative models with Tensorflow 1.x

Generative models (GAN, VAE, Diffusion Models, Autoregressive Models) implemented with Pytorch, Pytorch_lightning and hydra.

ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods

Simple implementation of Least Squares Generative Adversarial Networks

Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)

Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10

PyTorch implemented generative models for CelebA dataset: DCGAN, LSGAN, WGAN, WGANGP, InfoGAN, BEGAN, VAE, VAEGAN

Official PyTorch implementation on ID-GAN: High-Fidelity Synthesis with Disentangled Representation by Lee et al., 2020.

GANs Implementations in Keras

Semi-supervised InfoGAN

Playing with MNIST. Machine Learning. Generative Models.

The GAN Book: Train stable Generative Adversarial Networks using TensorFlow2, Keras and Python.

Beginner's Guide to building GAN from scratch with Tensorflow and Keras

Tutorial introduction slides to GANs. Code implementations and links of relevant papers.

Repository for my research on generative modelling of cell images

Keras implementation of InfoGAN (work in progress)