This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as a collaborator send me an email at [email protected].
Keras-GAN
Collection of Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. 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. Contributions and suggestions of GAN varieties to implement are very welcomed.
Trains a classifier on MNIST images that are translated to resemble MNIST-M (by performing unsupervised image-to-image domain adaptation). This model is compared to the naive solution of training a classifier on MNIST and evaluating it on MNIST-M. The naive model manages a 55% classification accuracy on MNIST-M while the one trained during domain adaptation gets a 95% classification accuracy.
$ cd pixelda/
$ python3 pixelda.py
Method
Accuracy
Naive
55%
PixelDA
95%
SGAN
Implementation of Semi-Supervised Generative Adversarial Network.
MVanderloo/Keras-GAN
This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as a collaborator send me an email at [email protected].
Keras-GAN
Collection of Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. 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. Contributions and suggestions of GAN varieties to implement are very welcomed.
See also: PyTorch-GAN
Table of Contents
Installation
Implementations
AC-GAN
Implementation of Auxiliary Classifier Generative Adversarial Network.
Code
Paper: https://arxiv.org/abs/1610.09585
Example
Adversarial Autoencoder
Implementation of Adversarial Autoencoder.
Code
Paper: https://arxiv.org/abs/1511.05644
Example
BiGAN
Implementation of Bidirectional Generative Adversarial Network.
Code
Paper: https://arxiv.org/abs/1605.09782
Example
BGAN
Implementation of Boundary-Seeking Generative Adversarial Networks.
Code
Paper: https://arxiv.org/abs/1702.08431
Example
CC-GAN
Implementation of Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks.
Code
Paper: https://arxiv.org/abs/1611.06430
Example
CGAN
Implementation of Conditional Generative Adversarial Nets.
Code
Paper:https://arxiv.org/abs/1411.1784
Example
Context Encoder
Implementation of Context Encoders: Feature Learning by Inpainting.
Code
Paper: https://arxiv.org/abs/1604.07379
Example
CoGAN
Implementation of Coupled generative adversarial networks.
Code
Paper: https://arxiv.org/abs/1606.07536
Example
CycleGAN
Implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
Code
Paper: https://arxiv.org/abs/1703.10593
Example
DCGAN
Implementation of Deep Convolutional Generative Adversarial Network.
Code
Paper: https://arxiv.org/abs/1511.06434
Example
DiscoGAN
Implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks.
Code
Paper: https://arxiv.org/abs/1703.05192
Example
DualGAN
Implementation of DualGAN: Unsupervised Dual Learning for Image-to-Image Translation.
Code
Paper: https://arxiv.org/abs/1704.02510
Example
GAN
Implementation of Generative Adversarial Network with a MLP generator and discriminator.
Code
Paper: https://arxiv.org/abs/1406.2661
Example
InfoGAN
Implementation of InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.
Code
Paper: https://arxiv.org/abs/1606.03657
Example
LSGAN
Implementation of Least Squares Generative Adversarial Networks.
Code
Paper: https://arxiv.org/abs/1611.04076
Example
Pix2Pix
Implementation of Image-to-Image Translation with Conditional Adversarial Networks.
Code
Paper: https://arxiv.org/abs/1611.07004
Example
PixelDA
Implementation of Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.
Code
Paper: https://arxiv.org/abs/1612.05424
MNIST to MNIST-M Classification
Trains a classifier on MNIST images that are translated to resemble MNIST-M (by performing unsupervised image-to-image domain adaptation). This model is compared to the naive solution of training a classifier on MNIST and evaluating it on MNIST-M. The naive model manages a 55% classification accuracy on MNIST-M while the one trained during domain adaptation gets a 95% classification accuracy.
SGAN
Implementation of Semi-Supervised Generative Adversarial Network.
Code
Paper: https://arxiv.org/abs/1606.01583
Example
SRGAN
Implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.
Code
Paper: https://arxiv.org/abs/1609.04802
Example
WGAN
Implementation of Wasserstein GAN (with DCGAN generator and discriminator).
Code
Paper: https://arxiv.org/abs/1701.07875
Example
WGAN GP
Implementation of Improved Training of Wasserstein GANs.
Code
Paper: https://arxiv.org/abs/1704.00028
Example