Keras-GAN.zip

<p align="center"> <img src="assets/keras_gan.png" width="480"\> </p> **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. <b>See also:</b> [PyTorch-GAN](https://github.com/eriklindernoren/PyTorch-GAN) ## Table of Contents * [Installation](#installation) * [Implementations](#implementations) + [Auxiliary Classifier GAN](#ac-gan) + [Adversarial Autoencoder](#adversarial-autoencoder) + [Bidirectional GAN](#bigan) + [Boundary-Seeking GAN](#bgan) + [Conditional GAN](#cgan) + [Context-Conditional GAN](#cc-gan) + [Context Encoder](#context-encoder) + [Coupled GANs](#cogan) + [CycleGAN](#cyclegan) + [Deep Convolutional GAN](#dcgan) + [DiscoGAN](#discogan) + [DualGAN](#dualgan) + [Generative Adversarial Network](#gan) + [InfoGAN](#infogan) + [LSGAN](#lsgan) + [Pix2Pix](#pix2pix) + [PixelDA](#pixelda) + [Semi-Supervised GAN](#sgan) + [Super-Resolution GAN](#srgan) + [Wasserstein GAN](#wgan) + [Wasserstein GAN GP](#wgan-gp) ## 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](acgan/acgan.py) Paper: https://arxiv.org/abs/1610.09585 #### Example ``` $ cd acgan/ $ python3 acgan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/acgan.gif" width="640"\> </p> ### Adversarial Autoencoder Implementation of _Adversarial Autoencoder_. [Code](aae/aae.py) Paper: https://arxiv.org/abs/1511.05644 #### Example ``` $ cd aae/ $ python3 aae.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/aae.png" width="640"\> </p> ### BiGAN Implementation of _Bidirectional Generative Adversarial Network_. [Code](bigan/bigan.py) Paper: https://arxiv.org/abs/1605.09782 #### Example ``` $ cd bigan/ $ python3 bigan.py ``` ### BGAN Implementation of _Boundary-Seeking Generative Adversarial Networks_. [Code](bgan/bgan.py) Paper: https://arxiv.org/abs/1702.08431 #### Example ``` $ cd bgan/ $ python3 bgan.py ``` ### CC-GAN Implementation of _Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks_. [Code](ccgan/ccgan.py) Paper: https://arxiv.org/abs/1611.06430 #### Example ``` $ cd ccgan/ $ python3 ccgan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/ccgan.png" width="640"\> </p> ### CGAN Implementation of _Conditional Generative Adversarial Nets_. [Code](cgan/cgan.py) Paper:https://arxiv.org/abs/1411.1784 #### Example ``` $ cd cgan/ $ python3 cgan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/cgan.gif" width="640"\> </p> ### Context Encoder Implementation of _Context Encoders: Feature Learning by Inpainting_. [Code](context_encoder/context_encoder.py) Paper: https://arxiv.org/abs/1604.07379 #### Example ``` $ cd context_encoder/ $ python3 context_encoder.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/context_encoder.png" width="640"\> </p> ### CoGAN Implementation of _Coupled generative adversarial networks_. [Code](cogan/cogan.py) Paper: https://arxiv.org/abs/1606.07536 #### Example ``` $ cd cogan/ $ python3 cogan.py ``` ### CycleGAN Implementation of _Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks_. [Code](cyclegan/cyclegan.py) Paper: https://arxiv.org/abs/1703.10593 <p align="center"> <img src="http://eriklindernoren.se/images/cyclegan.png" width="640"\> </p> #### Example ``` $ cd cyclegan/ $ bash download_dataset.sh apple2orange $ python3 cyclegan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/cyclegan_gif.gif" width="640"\> </p> ### DCGAN Implementation of _Deep Convolutional Generative Adversarial Network_. [Code](dcgan/dcgan.py) Paper: https://arxiv.org/abs/1511.06434 #### Example ``` $ cd dcgan/ $ python3 dcgan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/dcgan2.png" width="640"\> </p> ### DiscoGAN Implementation of _Learning to Discover Cross-Domain Relations with Generative Adversarial Networks_. [Code](discogan/discogan.py) Paper: https://arxiv.org/abs/1703.05192 <p align="center"> <img src="http://eriklindernoren.se/images/discogan_architecture.png" width="640"\> </p> #### Example ``` $ cd discogan/ $ bash download_dataset.sh edges2shoes $ python3 discogan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/discogan.png" width="640"\> </p> ### DualGAN Implementation of _DualGAN: Unsupervised Dual Learning for Image-to-Image Translation_. [Code](dualgan/dualgan.py) Paper: https://arxiv.org/abs/1704.02510 #### Example ``` $ cd dualgan/ $ python3 dualgan.py ``` ### GAN Implementation of _Generative Adversarial Network_ with a MLP generator and discriminator. [Code](gan/gan.py) Paper: https://arxiv.org/abs/1406.2661 #### Example ``` $ cd gan/ $ python3 gan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/gan_mnist5.gif" width="640"\> </p> ### InfoGAN Implementation of _InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets_. [Code](infogan/infogan.py) Paper: https://arxiv.org/abs/1606.03657 #### Example ``` $ cd infogan/ $ python3 infogan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/infogan.png" width="640"\> </p> ### LSGAN Implementation of _Least Squares Generative Adversarial Networks_. [Code](lsgan/lsgan.py) Paper: https://arxiv.org/abs/1611.04076 #### Example ``` $ cd lsgan/ $ python3 lsgan.py ``` ### Pix2Pix Implementation of _Image-to-Image Translation with Conditional Adversarial Networks_. [Code](pix2pix/pix2pix.py) Paper: https://arxiv.org/abs/1611.07004 <p align="center"> <img src="http://eriklindernoren.se/images/pix2pix_architecture.png" width="640"\> </p> #### Example ``` $ cd pix2pix/ $ bash download_dataset.sh facades $ python3 pix2pix.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/pix2pix2.png" width="640"\> </p> ### PixelDA Implementation of _Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks_. [Code](pixelda/pixelda.py) 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. ``` $ cd pixelda/ $ python3 pixelda.py ``` | Method | Accuracy | | ------------ |:---------:| | Naive | 55% | | PixelDA | 95% | ### SGAN Implementation of _Semi-Supervised Generative Adversarial Network_. [Code](sgan/sgan.py) Paper: https://arxiv.org/abs/1606.01583 #### Example ``` $ cd sgan/ $ python3 sgan.py ``` <p align="center"> <img src="http://eriklindernoren.se/images/sgan.png" width="640"\> </p> ### SRGAN Implementation of _Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network_. [Code](srgan/srgan.py) Paper: https://arxiv.org/abs/1609.04802