There are 4 repositories under wgan-gp topic.
Collection of generative models in Tensorflow
Collection of generative models in Pytorch version.
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch
Awesome Generative Adversarial Networks with tensorflow
Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking
Chainer implementation of recent GAN variants
Simple Implementation of many GAN models with PyTorch.
Pytorch implementation of Wasserstein GANs with Gradient Penalty
PyTorch implementation of DCGAN, WGAN-GP and SNGAN.
Implementation of some different variants of GANs by tensorflow, Train the GAN in Google Cloud Colab, DCGAN, WGAN, WGAN-GP, LSGAN, SNGAN, RSGAN, RaSGAN, BEGAN, ACGAN, PGGAN, pix2pix, BigGAN
DCGAN LSGAN WGAN-GP DRAGAN PyTorch
GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.
A Tensorflow implementation of GAN, WGAN and WGAN with gradient penalty.
implementation of several GANs with pytorch
Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018
🚀 Variants of GANs most easily implemented as TensorFlow2. GAN, DCGAN, LSGAN, WGAN, WGAN-GP, DRAGAN, ETC...
A PyTorch implementation of SRGAN specific for Anime Super Resolution based on "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network". And another PyTorch WGAN-gp implementation of SRGAN referring to "Improved Training of Wasserstein GANs".
Repository for implementation of generative models with Tensorflow 1.x
TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.
Improved training of Wasserstein GANs
Pytorch implementation of a Conditional WGAN with Gradient Penalty
speech enhancement GAN on waveform/log-power-spectrum data using Improved WGAN
Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10
Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)
Pure tensorflow implementation of progressive growing of GANs
Implementation of our paper "Wasserstein Adversarial Transformer for Cloud Workload Prediction"
[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.
TensorFlow 2.0 implementation of Improved Training of Wasserstein GANs
Generative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)
Tensorflow Implementation of Paper "Improved Training of Wasserstein GANs"