Collection of generative models in Tensorflow

Collection of generative models in Pytorch version.

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

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.

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.

PyTorch Implementation of InfoGAN

🎎 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.

Repository for implementation of generative models with Tensorflow 1.x

Simple implementation of Least Squares Generative Adversarial Networks

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

GANs Implementations in Keras

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

Semi-supervised InfoGAN

Playing with MNIST. Machine Learning. Generative Models.

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

Repository for my research on generative modelling of cell images

Keras implementation of InfoGAN (work in progress)

Implement multiple gan including vanilla_gan, dcgan, cgan, infogan and wgan with tensorflow and dataset including mnist.

Tensorflow implementation of Generative Adversarial Networks

InfoGAN implementation using tensorflow

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

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

GAN / DCGAN / InfoGAN / BEGAN ...

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

This is a deep learning code written in PyTorch that convert a given text into image.