Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. Since I'm training these models on my Macbook Pro they will be limited in their complexity (and therefore the quality of the generated images) compared to the implementations suggested in the papers. Short training sessions are prioritized.

• Keras-GAN
  • About
  • Table of Contents
  • Implementations
    • Auxiliary Classifier GAN
    • Adversarial Autoencoder
    • Bidirectional GAN
    • Context-Conditional GAN
    • Context Encoder
    • Deep Convolutional GAN
    • Generative Adversarial Network
    • InfoGAN
    • Semi-Supervised GAN
    • Wasserstein GAN

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

Inpainting using a GAN where the generator is conditioned on a randomly masked image. In this implementation images of dogs and cats taken from the Cifar-10 dataset are used. These images are of very low resolution and the results are therefore not as nice as in the implementation described in the paper. In this implementation I have also decided to combine the adversarial loss with an l2 loss which measures the generated image's similarity to the original images. These losses are weighted similar to the approach described by Pathak et al. (2016)
in their paper Context Encoders: Feature Learning by Inpainting.

Code

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

Implementation of Context Encoders: Feature Learning by Inpainting.

Code

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

Implementation of Deep Convolutional Generative Adversarial Network.

Code

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

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

Code

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

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

Code

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

Implementation of Semi-Supervised Generative Adversarial Network.

Code

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

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

Code

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