A collection of GAN models for generating synthetic data
-
Updated
Mar 10, 2023 - Python
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
A collection of GAN models for generating synthetic data
Generating images based on abstract art with a help of Generative Adversarial Networks. Version: 1.25
Generation images of chest x-ray using GAN
Using Generative Adversarial Networks to generate bird photos.
This repository contains a Convolutional GAN which generates faces based on the CelebFaces dataset.
PyTorch implementations of Generative Adversarial Networks.
Tensorflow and Keras implementation of GAN+VAE generative model.
The experimental codes using Keras from the paper that was accepted to GECCO 2019.
a PyTorch implementation - handwriting number recognition with GAN model using MNIST Dataset
This work demonstrates the simple and straight forward way of using the DC-GAN architecture to obtain bright new and unique ’waifus’ the heart can desire.
A repository for codes and projects related to Generative Models including: Autoencoders, VAEs, GANs, RBM
PyTorch implementations of simple GANs for lightweight generative modeling of input distributions.
Web application to generate anime avatars
Synthetic Data Generation by Very Basic 1-D GAN
Generative adversarial network for creating images of handwritten digits by a neural network
Python implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) for generating realistic fake faces using PyTorch.
Various implementations of GAN using Pytorch
Tensorflow implementation of Conditional GAN trained on MNIST dataset
Implementation of GAN using TensorFlow's Keras.
Released June 10, 2014