A Tensorflow Implementation of Generative Adversarial Networks as presented in the original paper by Goodfellow et. al. (https://arxiv.org/abs/1406.2661)
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Updated
Jan 26, 2017 - 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 Tensorflow Implementation of Generative Adversarial Networks as presented in the original paper by Goodfellow et. al. (https://arxiv.org/abs/1406.2661)
Various Preprocessing tools for use with Generative Adversarial Networks
Papers, codes, slides and blogs about Generative Adversrial Nets.
GAN model using PyTorch
Tensorflow implementation of Generative Adversarial Network for approximating a 1D Gaussian distribution
Quite **simple and clear** GAN Example to simulate gaussian curve.
Udacity Nanodegree - Deep Learning - Projects: Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks
An implementation of DiscoGAN in tensorflow
Generate realistic synthetic images using unsupervised learning techniques of Generative Adversarial Networks (GANs)
A PyTorch implementation of alpha-GAN
PyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)
PyTorch implementation of CycleGAN
A high-level framework for advanced deep learning with TensorFlow
Keras implementation of Deep Convolutional Generative Adversarial Networks (vanilla flavor).
GAN Code for presentation
Semi-supervised Learning GAN
Image Inpainting using Context Encoders
Project to transform a natural language description into an image using Generative Adversarial Networks.
Released June 10, 2014