lsgan

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.

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

DCGAN LSGAN WGAN-GP DRAGAN PyTorch

🚀 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

【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

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

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

[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.

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.