wasserstein-gan
There are 90 repositories under wasserstein-gan topic.
GAN-s
All GAN models in Keras
SigFiltering
Sampling from the solution of the Zakai equation, using the Signature and Conditional Wasserstein GANs
frame-prediction-pytorch
PyTorch implementation of WGAN-GP-based video generation. Includes functionality for measuring Frechet Video Distance and implementing recent research improvements of WGAN-GP. Read paper at https://github.com/talcron/frame-prediction-pytorch/blob/media/paper.pdf
WGAN-TF2.x
Tensorflow2.x implementation of Wasserstein GAN
CNN_Implementations
Data and Trained models can be downloaded from https://goo.gl/7PrKD2
WGAN-TF
TensorFlow implementation of Wasserstein GAN (WGAN) with MNIST dataset.
DL4S-WGAN-GP
Wasserstein GAN with Gradient Penalty in DL4S
WGAN-GradientPenalty
A Pytorch implementation demo for WGAN-GP in order to generate handwritten digits(MNIST dataset) Pytorch构建WGAN-GP网络实现手写数字生成(MNIST数据集)
Generative_Adversarial_Networks
Implementation of some types of GANs (Deep convolutional GAN - Wasserstein GAN - conditional GAN) with PyTorch library
progressive_gan
Progressive Growing of GANS
Shoe-GAN
Generating shoes with GANs in sake of lulz and education
WGAN_keras
Wasserstein GAN implementation with keras
DeblurGAN_ipynb_simplified
This is a very simplified ipynb code for KupynOrest's Deblur GAN code. DeblurGAN addresses the challenge of end-to-end image deblurring through the use of conditional Generative Adversarial Networks (cGANs).I have used pytorch for this implementation.
Abstract-Art-Gallery-DCGAN
Using DCGAN to generate abstract art
music-generation-gan
Music Generation using Symbolic Representation with Generative Adversarial Networks (GANs)
Deep-Learning-Models
Keras implementation of Deep Learning Models applied to the MNIST and Polynomial datasets. Repository for the Software and Computing for Nuclear and Subnuclear Physics Project.
WGAN-with-Feedback-Cross-Attention-LayerNorm
WGAN with feedback from discriminator& LayerNorm instead of BatchNorm
CS294-158
Homeworks from CS294-158-19 (Deep unsupervised learning) implemented in Pytorch
WGANGP-Presentation
Materials for my presentation on 17/07/2019 in Hong Kong Machine Learning Meetup. The topic is WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty)
Generative-Adversarial-Networks
Implementations of various architectures and implementations of Generative Adversarial Networks
generative_neural_networks
Investigation into Generative Neural Networks.
tf-gans
Major GANs are implemented in this repository 🔥
Atari-WGAN
Implementation of WGAN to generation of Atari Games Images. (GAN, WGAN, ATARI, Generative)
WassersteinGAN
Tensorflow Wasserstein GAN implementation with TFRecord data format. WGAN, WGAN-GP (gradient penalty), DCGAN and showing example usage with CelebA dataset.
percivaltts
ATTENTION! This is a mirror of the following GitLab project:
WGAN-GP
Implementation of Wassertein GAN with Gradient Penalty in PyTorch
WGAN-s-Wasserstein-Gen.AI-
Wasserstein GAN (WGAN) is a variant of the traditional Generative Adversarial Network (GAN) that aims to improve training stability and address issues like mode collapse.
DeepMelSpectrogramGenerator
The mel spectrogram generator using conditional WGAN-GP. For the mel spectrogram inverter, look up HiFi-GAN
Generative-Adversarial-Network-Pizzas-
A generative adversarial network engineered that utilizes a discriminator and a generator. The GAN can be trained using a Binary Cross Entropy Loss or a Wasserstein Distance Loss to generate replicate images based on input data.
Generative-Adversarial-Networks
This repository contains different GAN architectures for data generation and posterior sampling to solve inverse problems featured in my Master's Thesis.
WGAN-learns-the-distributon-of-a-MoG
A Wasserstein Generative Adversarial Network that learns the distribution of a Mixture of Gaussian, using weight clipping or spectral normalization
pokeGAN
WGAN-GP Implementation of Pokémon Image Dataset
Investigate_WAE_in_BrainMRI
This research project intent is to review and demonstrate a comparability among recent auto-encoder methods by utilizing single architecture and resolution. Each method will be ranked based on selective performance measure in modeling healthy brain and the sensitivity towards domain shift.
MNIST-WGAN
A C# WGAN.
GAN
This 'Generative Adversarial Network' project was implemented in grad course CSE-676 : Deep Learning [Fall 2019 @UB_SUNY] Course Instructor : Sargur N. Srihari(https://cedar.buffalo.edu/~srihari/)