tuananhbui89 / gaan

Tensorflow implementation of Generative Adversarial Autoencoder Networks (GAAN)

Code for experiments in Generative Adversarial Autoencoder Networks (GAAN)

Setup

Prerequisites

Python, Numpy, Tensorflow

Getting Started

We conduct experiments of our model with 1D/2D synthetic data, MNIST, CelebA and CIFAR-10 datasets.

1D demo

In addition to GAAN, other methods, such as GAN, MDGAN, VAEGAN, WGAN-GP are provided in our code.

>> cd gaan_toy1d
>> python gan_toy1d.py

Quick video demos, you can reproduce easily these videos with our code:

GAN
WGANGP (WGAN-GP can match data distribution at some time, but diverged later)
VAEGAN
GAAN

Our 1D code is based on 1D demo references:

[1] https://github.com/kremerj/gan
[2] http://notebooks.aylien.com/research/gan/gan_simple.html

2D synthetic data

>> cd gaan_toy2d
>> python gaan_toy2d.py

We provide three different data layouts you can test on: 'SINE' 'PLUS' 'SQUARE'. Just change the parameter testcase in the code gaan_toy2d.py. For example:

testcase      = 'SQUARE'

Image data (MNIST, CelebA and CIFAR-10)

We provide our code for image datasets, such as: MNIST, CelebA and CIFAR-10.

MNIST

>> cd gaan_image
>> python gaan_mnist.py

Samples generated by our GAAN model (left) and real samples (right).

CIFAR-10

Downloading cifar-10 from 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' and extracting it into the correct folder: eg. ./data/cifar10/

>> cd gaan_image
>> python gaan_cifar.py

Samples generated by our GAAN model (left) and real samples (right).

CelebA

Downloading cifar-10 from: https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg and extract into the correct folder: eg. ./data/celeba/

>> cd gaan_image
>> python gaan_celeba.py

Samples generated by our GAAN model (left) and real samples (right).

Citation

If you use our code in your research, please cite our paper:

@article{trung2018gaan,
  title={Generative Adversarial Autoencoder Networks},
  author={Ngoc-Trung Tran and Tuan-Anh Bui and Ngai-Man Cheung},
  journal={arXiv preprint arXiv:1803.08887},
  year={2018}
}