wasserstein-gan
There are 90 repositories under wasserstein-gan topic.
yfeng95/GAN
Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN
EmilienDupont/wgan-gp
Pytorch implementation of Wasserstein GANs with Gradient Penalty
arunppsg/TadGAN
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"
jiamings/cramer-gan
Tensorflow Implementation on "The Cramer Distance as a Solution to Biased Wasserstein Gradients" (https://arxiv.org/pdf/1705.10743.pdf)
for-ai/CipherGAN
TensorFlow implementation of CipherGAN
tlatkowski/inpainting-gmcnn-keras
Keras implementation of "Image Inpainting via Generative Multi-column Convolutional Neural Networks" paper published at NIPS 2018
andreaferretti/ganzo
A GAN framework
tensorfreitas/DCGAN-for-Bird-Generation
DCGAN and WGAN implementation on Keras for Bird Generation
RahulBhalley/progressive-growing-of-gans.pytorch
ProGAN with Standard, WGAN, WGAN-GP, LSGAN, BEGAN, DRAGAN, Conditional GAN, InfoGAN, and Auxiliary Classifier GAN training methods
fonfonx/WassersteinGAN.torch
Torch implementation of Wasserstein GAN https://arxiv.org/abs/1701.07875
afrozas/skip-thought-gan
Generating Text through Adversarial Training(GAN) using Skip-Thought Vectors
ChristophReich1996/Mode_Collapse
Mode collapse example of GANs in 2D (PyTorch).
fmu2/Wasserstein-BiGAN
Wasserstein BiGAN (Bidirectional GAN trained using Wasserstein distance)
preritj/progressive_growing_of_GANs
Pure tensorflow implementation of progressive growing of GANs
linksense/ConvolutionaNeuralNetworksToEnhanceCodedSpeech
In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to G.711, even en par with uncoded speech.
drewszurko/tensorflow-WGAN-GP
TensorFlow 2.0 implementation of Improved Training of Wasserstein GANs
laurahanu/Improved-Wasserstein-GAN-application-on-MRI-images
Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images
shaform/DeepNetworks
My implementations of deep neural networks for practice.
fukuta0614/chainer-image-generation
chainer implementation of VAE-GAN, Wasserstein GAN (WGAN), CycleGAN
hvy/chainer-wasserstein-gan
Chainer implementation of the Wesserstein GAN
vsooda/mxnet-wgan
mxnet implement for Conditional Wasserstein GAN
nardeas/MHGAN-Tensorflow
Metropolis-Hastings GAN in Tensorflow for enhanced generator sampling
giocoal/ADNI-brain-MRI-alzheimer-WGAN-generation-and-classification
Brain T1-Weighted MRI Images Classification and WGAN Generation (Alzheimer's and Healthy patients) for the purpose of data augmentation. Implemented in TensorFlow, trained on ADNI dataset.
KnetML/WGAN.jl
Implementation of Wasserstein GAN using Knet
dr-costas/undaw
Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance
georgehalal/cWGAN-GP
A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys
RahulBhalley/turing-gan
Source code for "Training Generative Adversarial Networks Via Turing Test".
hiwonjoon/wae-wgan
Reimplementation of Wasserstein Auto Encoder (WAE) with Wasserstein GAN based penalty D_Z in Tensorflow
kpandey008/wasserstein-gans
Implementation of Wasserstein Generative Adversarial Networks using Tensorflow
Data-Science-kosta/WGAN-GP-for-Face-Generation
Keras implementation of WGAN GP for face generation. The model is trained on CelebA dataset.
mickypaganini/GAN_tutorial
Vanilla GAN and WGAN implementations in PyTorch on the FashionMNIST dataset
cedrickchee/wasserstein-gan
PyTorch implementation of Wasserstein GAN paper
MeetGandhi/Reconstruction-of-Trajectory-recorded-with-Missing-Markers
This repository deals with analyzing various Neural Network approaches and finding the one with the most accurate reconstruction of motion captured trajectories recorded with missing markers in softwares like Vicon Nexus
NicelyCla/cWGAN-gp
My version of cWGAN-gp. Simply my cDCGAN-based but using the Wasserstein Loss and gradient penalty.
NicelyCla/GAN-and-Meta-Learning
We've applied the Reptile algorithm to our GAN architectures. The peculiarity is the exclusion of G from meta-learning. Surprisingly, everything worked and the research was published in a paper. More details reported on the paper "Towards Latent Space Optimization of GANs Using Meta-Learning" and the thesis (Italian).