wgan-gp
There are 153 repositories under wgan-gp topic.
ddbourgin/numpy-ml
Machine learning, in numpy
hwalsuklee/tensorflow-generative-model-collections
Collection of generative models in Tensorflow
znxlwm/pytorch-generative-model-collections
Collection of generative models in Pytorch version.
tjwei/GANotebooks
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch
caogang/wgan-gp
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
kozistr/Awesome-GANs
Awesome Generative Adversarial Networks with tensorflow
kwotsin/mimicry
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
khanrc/tf.gans-comparison
Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking
jalola/improved-wgan-pytorch
Improved WGAN in Pytorch
pfnet-research/chainer-gan-lib
Chainer implementation of recent GAN variants
Yangyangii/GAN-Tutorial
Simple Implementation of many GAN models with PyTorch.
EmilienDupont/wgan-gp
Pytorch implementation of Wasserstein GANs with Gradient Penalty
w86763777/pytorch-gan-collections
PyTorch implementation of DCGAN, WGAN-GP and SNGAN.
MingtaoGuo/DCGAN_WGAN_WGAN-GP_LSGAN_SNGAN_RSGAN_BEGAN_ACGAN_PGGAN_TensorFlow
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
LynnHo/DCGAN-LSGAN-WGAN-GP-DRAGAN-Pytorch
DCGAN LSGAN WGAN-GP DRAGAN PyTorch
krishk97/ECE-C247-EEG-GAN
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.
LixiangHan/GANs-for-1D-Signal
implementation of several GANs with pytorch
lilianweng/unified-gan-tensorflow
A Tensorflow implementation of GAN, WGAN and WGAN with gradient penalty.
wangguanan/Pytorch-Basic-GANs
Simple Pytorch implementations of most used Generative Adversarial Network (GAN) varieties.
marload/GANs-TensorFlow2
🚀 Variants of GANs most easily implemented as TensorFlow2. GAN, DCGAN, LSGAN, WGAN, WGAN-GP, DRAGAN, ETC...
tlatkowski/inpainting-gmcnn-keras
Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018
goldhuang/SRGAN-PyTorch
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".
Kyushik/Generative-Model
Repository for implementation of generative models with Tensorflow 1.x
shaohua0116/WGAN-GP-TensorFlow
TensorFlow implementations of Wasserstein GAN with Gradient Penalty (WGAN-GP), Least Squares GAN (LSGAN), GANs with the hinge loss.
Lornatang/WassersteinGAN_GP-PyTorch
Improved training of Wasserstein GANs
gcucurull/cond-wgan-gp
Pytorch implementation of a Conditional WGAN with Gradient Penalty
jerrygood0703/speech-enhancement-WGAN
speech enhancement GAN on waveform/log-power-spectrum data using Improved WGAN
AliceAria/Performance-comparison-of-GAN-on-cifar-10
Performance comparison of ACGAN, BEGAN, CGAN, DRAGAN, EBGAN, GAN, infoGAN, LSGAN, VAE, WGAN, WGAN_GP on cifar-10
kartikgill/TF2-Keras-GAN-Notebooks
Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)
preritj/progressive_growing_of_GANs
Pure tensorflow implementation of progressive growing of GANs
shivaniarbat/wgan-gp-transformer
Implementation of our paper "Wasserstein Adversarial Transformer for Cloud Workload Prediction"
sutd-visual-computing-group/Fourier-Discrepancies-CNN-Detection
[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.
drewszurko/tensorflow-WGAN-GP
TensorFlow 2.0 implementation of Improved Training of Wasserstein GANs
Machine-Learning-Tokyo/generative_deep_learning
Generative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)
akashsdoshi96/ota-gan-mimo-ce
Implementation of "Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation"