Popular GitHub Repositories for Wasserstein Gan

Pytorch implementation of Wasserstein GANs with Gradient Penalty

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Tensorflow Implementation on "The Cramer Distance as a Solution to Biased Wasserstein Gradients" (https://arxiv.org/pdf/1705.10743.pdf)

TensorFlow implementation of CipherGAN

A GAN framework

DCGAN and WGAN implementation on Keras for Bird Generation

Torch implementation of Wasserstein GAN https://arxiv.org/abs/1701.07875

Mode collapse example of GANs in 2D (PyTorch).

Generating Text through Adversarial Training(GAN) using Skip-Thought Vectors

Wasserstein BiGAN (Bidirectional GAN trained using Wasserstein distance)

Pure tensorflow implementation of progressive growing of GANs

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 pos

Improved Wasserstein GAN (WGAN-GP) application on medical (MRI) images

My implementations of deep neural networks for practice.

chainer implementation of VAE-GAN, Wasserstein GAN (WGAN), CycleGAN

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.

Source code for "Training Generative Adversarial Networks Via Turing Test".

Implementation of Wasserstein Generative Adversarial Networks using Tensorflow

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

TensorFlow implementation of Wasserstein GAN (WGAN) with MNIST dataset.