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.

• 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
    • DiscoGAN
    • DualGAN
    • Generative Adversarial Network
    • InfoGAN
    • LSGAN
    • Pix2Pix
    • Semi-Supervised GAN
    • Super-Resolution GAN
    • Wasserstein GAN

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

Implementation of Auxiliary Classifier Generative Adversarial Network.

Code

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

Implementation of Adversarial Autoencoder.

Code

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

Implementation of Bidirectional Generative Adversarial Network.

Code

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

Implementation of Boundary-Seeking Generative Adversarial Networks.

Code

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

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

Code

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

Implementation of Conditional Generative Adversarial Nets.

Code

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

Implementation of Context Encoders: Feature Learning by Inpainting.

Code

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

Implementation of Coupled generative adversarial networks.

Code

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

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

Implementation of Deep Convolutional Generative Adversarial Network.

Code

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

Implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1703.05192

$ cd discogan
$ bash download_dataset.sh edges2shoes
$ python3 discogan.py

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

Code

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

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

Code

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

GAN on RGB face images Code

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

Code

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

Implementation of Least Squares Generative Adversarial Networks.

Code

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

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

Implementation of Semi-Supervised Generative Adversarial Network.

Code

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

Implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1609.04802

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

Code

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