jazzman37/Keras-GAN

Keras implementations of Generative Adversarial Networks.

Keras-GAN

About

Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. If dense layers produce reasonable results for a given model I will often prefer them over convolutional layers. The reason is that I would like to enable people without GPUs to test these implementations out. These models are in some cases simplified versions of the ones ultimately described in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. However, because of this the results will not always be as nice as in the papers.

Table of Contents

• Keras-GAN
  • About
  • Table of Contents
  • Installation
  • Implementations
    • Auxiliary Classifier GAN
    • Adversarial Autoencoder
    • Bidirectional GAN
    • Boundary-Seeking GAN
    • Conditional GAN
    • Context-Conditional GAN
    • Context Encoder
    • Coupled GANs
    • CycleGAN
    • Deep Convolutional GAN
    • DualGAN
    • Generative Adversarial Network
    • InfoGAN
    • LSGAN
    • Pix2Pix
    • Semi-Supervised GAN
    • Wasserstein GAN

Installation

$ git clone https://github.com/eriklindernoren/Keras-GAN
$ cd Keras-GAN
$ sudo pip3 install -r requirements.txt

Implementations

AC-GAN

Implementation of Auxiliary Classifier Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1610.09585

Adversarial Autoencoder

Implementation of Adversarial Autoencoder.

Code

Paper: https://arxiv.org/abs/1511.05644

BiGAN

Implementation of Bidirectional Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1605.09782

BGAN

Implementation of Boundary-Seeking Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1702.08431

CC-GAN

Implementation of Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1611.06430

CGAN

Implementation of Conditional Generative Adversarial Nets.

Code

Paper:https://arxiv.org/abs/1411.1784

Context Encoder

Implementation of Context Encoders: Feature Learning by Inpainting.

Code

Paper: https://arxiv.org/abs/1604.07379

CoGAN

Implementation of Coupled generative adversarial networks.

Code

Paper: https://arxiv.org/abs/1606.07536

CycleGAN

Implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1703.10593

$ cd cyclegan
$ bash download_dataset.sh apple2orange
$ python3 cyclegan.py

DCGAN

Implementation of Deep Convolutional Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1511.06434

DualGAN

Implementation of DualGAN: Unsupervised Dual Learning for Image-to-Image Translation.

Code

Paper: https://arxiv.org/abs/1704.02510

GAN

Implementation of Generative Adversarial Network with a MLP generator and discriminator.

Code

Paper: https://arxiv.org/abs/1406.2661

InfoGAN

Implementation of InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.

Code

Paper: https://arxiv.org/abs/1606.03657

LSGAN

Implementation of Least Squares Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1611.04076

Pix2Pix

Implementation of Unpaired Image-to-Image Translation with Conditional Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1611.07004

$ cd pix2pix
$ bash download_dataset.sh facades
$ python3 pix2pix.py

SGAN

Implementation of Semi-Supervised Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1606.01583

WGAN

Implementation of Wasserstein GAN (with DCGAN generator and discriminator).

Code

Paper: https://arxiv.org/abs/1701.07875