Collection of generative models in Tensorflow

Collection of generative models in Pytorch version.

Awesome Generative Adversarial Networks with tensorflow

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

Reimplementation of GANs

DCGAN LSGAN WGAN-GP DRAGAN PyTorch

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

Chainer implementation of Least Squares GAN (LSGAN)

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

TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.

Simple implementation of Least Squares Generative Adversarial Networks

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

[CVPR 2021: Oral] In this work, we show that high frequency Fourier spectrum decay discrepancies are not inherent characteristics for existing CNN-based generative models.

Playing with MNIST. Machine Learning. Generative Models.

From scratch, simple and easy-to-understand Pytorch implementation of various generative adversarial network (GAN): GAN, DCGAN, Conditional GAN (cGAN), WGAN, WGAN-GP, CycleGAN, LSGAN, and StarGAN.

Least Squares Generative Adversarial Network implemented in Chainer

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

This repository contains the files used for our Interspeech 2017 paper.

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

implement GANs and VAE using pytorch