Collection of generative models in Tensorflow

Collection of generative models in Pytorch version.

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

Reimplementation of GANs

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

DCGAN LSGAN WGAN-GP DRAGAN PyTorch

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

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

Repository for implementation of generative models with Tensorflow 1.x

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.

【X世纪星际终端】A Wechat Social and AR Game: 基于微信聊天，结合增强现实技术AR+LBS（基于图像位置）的轻社交星际漂流瓶游戏。向外太空发送漂流信息，看看AI预测的外星人是长什么样的，寻找身边的外星人,逗逗外星生物，看看外星植物及外星建筑。Send the message to the outer space, find the aliens in the earth. Let`s see what they look like from LSGAN`s prediction. Also, Have a look at the aliens' pets and the vegetation from the outer space

Simple implementation of Least Squares Generative Adversarial Networks

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

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.