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https://github.com/eriklindernoren/Keras-GAN

Keras implementations of Generative Adversarial Networks.
https://github.com/eriklindernoren/Keras-GAN

deep-learning gan generative-adversarial-networks keras neural-networks

Last synced: about 2 months ago
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Keras implementations of Generative Adversarial Networks.

Host: GitHub
URL: https://github.com/eriklindernoren/Keras-GAN
Owner: eriklindernoren
License: mit
Created: 2017-07-11T16:24:53.000Z (about 7 years ago)
Default Branch: master
Last Pushed: 2022-12-12T05:55:51.000Z (almost 2 years ago)
Last Synced: 2024-06-25T20:53:43.328Z (3 months ago)
Topics: deep-learning, gan, generative-adversarial-networks, keras, neural-networks
Language: Python
Homepage:
Size: 86.3 MB
Stars: 9,151
Watchers: 276
Forks: 3,148
Open Issues: 143
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesomeai - Keras-GAN
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awesome-ai-awesomeness - Keras-GAN
awesome-GAN-papers - 12
awesome-keras - Keras-GAN - A Keras implementation of Generative Adversarial Networks. (Examples/Notebooks)

README

        


    



**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](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

```



    



### Adversarial Autoencoder

Implementation of _Adversarial Autoencoder_.

[Code](aae/aae.py)

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

#### Example

```

$ cd aae/

$ python3 aae.py

```



    



### 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

```



    



### CGAN

Implementation of _Conditional Generative Adversarial Nets_.

[Code](cgan/cgan.py)

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

#### Example

```

$ cd cgan/

$ python3 cgan.py

```



    



### 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

```



    



### 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



    



#### Example

```

$ cd cyclegan/

$ bash download_dataset.sh apple2orange

$ python3 cyclegan.py

```   



    



### 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

```



    



### DiscoGAN

Implementation of _Learning to Discover Cross-Domain Relations with Generative Adversarial Networks_.

[Code](discogan/discogan.py)

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



    



#### Example

```

$ cd discogan/

$ bash download_dataset.sh edges2shoes

$ python3 discogan.py

```   



    



### 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

```



    



### 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

```



    



### 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



    



#### Example

```

$ cd pix2pix/

$ bash download_dataset.sh facades

$ python3 pix2pix.py

```   



    



### 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

```



    



### 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



    



#### Example

```

$ cd srgan/

$ python3 srgan.py

```



    



### WGAN

Implementation of _Wasserstein GAN_ (with DCGAN generator and discriminator).

[Code](wgan/wgan.py)

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

#### Example

```

$ cd wgan/

$ python3 wgan.py

```



    



### WGAN GP

Implementation of _Improved Training of Wasserstein GANs_.

[Code](wgan_gp/wgan_gp.py)

Paper: https://arxiv.org/abs/1704.00028

#### Example

```

$ cd wgan_gp/

$ python3 wgan_gp.py

```