Generative Adversarial Networks implementation in Chainer
-
Updated
Jan 30, 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.
Generative Adversarial Networks implementation in Chainer
Repository of code examples for Generative Adversarial Networks implemented in Tensorflow
Implement DCGAN on MNIST.
Course/Homework materials for the "Creative Applications of Deep Learning with Tensorflow" MOOC
Tensorflow implementation of GAN architectures.
Auxiliary Classifier Generative Adversarial Network in Torch7
Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875
Pix2Pix Image translation using conditional generative adversarial network - sketch to face
Keras implementation of Deep Convolutional Generative Adversarial Networks, code run base on tensorflow or theano
Generative Adversarial Networks (GANs) in PyTorch
An implementation of VAEGAN (variational autoencoder + generative adversarial network).
DCGAN on LSUN
A curated list of awesome papers, tutorials, videos, conferences, libraries, software about generative models.
Implementation of Ian Goodfellow's paper on "Generative Adversarial Networks (GAN)".
A pytorch implementation of the Domain Transfer Network (DTN), Unsupervised Cross-Domain Image Generation
Data augmentation for Captcha
200+ MPH Agile Drone
Collection of Generative models in TensorFlow
Released June 10, 2014