<p align="center">
<img src="assets/keras_gan.png" width="480"\>
</p>
## 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
<p align="center">
<img src="http://eriklindernoren.se/images/superresgan.png" width="640"\>
</p>
#### Example
```
$ cd srgan/
<follow steps at the top of srgan.py>
$ python3 srgan.py
```
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keras_Gan_v1.rar (79个子文件)
keras_Gan
dualgan
dualgan.py 7KB
images
.gitignore 13B
saved_model
.gitignore 13B
cogan
images
.gitignore 13B
saved_model
.gitignore 13B
cogan.py 7KB
新建文本文档.txt 0B
srgan
srgan.py 10KB
images
.gitignore 13B
saved_model
.gitignore 13B
data_loader.py 1KB
lsgan
images
.gitignore 13B
lsgan.py 5KB
saved_model
.gitignore 13B
acgan
acgan.py 8KB
images
.gitignore 13B
saved_model
.gitignore 13B
sgan
images
.gitignore 13B
sgan.py 7KB
saved_model
.gitignore 13B
dcgan
dcgan.py 6KB
images
.gitignore 13B
saved_model
.gitignore 13B
requirements.txt 136B
infogan
infogan.py 8KB
images
.gitignore 13B
saved_model
.gitignore 13B
pixelda
test.py 782B
images
.gitignore 13B
saved_model
.gitignore 13B
pixelda.py 8KB
data_loader.py 3KB
cyclegan
cyclegan.py 10KB
download_dataset.sh 824B
images
.gitignore 13B
saved_model
.gitignore 13B
data_loader.py 3KB
gan
gan.py 5KB
images
.gitignore 13B
saved_model
.gitignore 13B
bgan
images
.gitignore 13B
bgan.py 5KB
saved_model
.gitignore 13B
filename.png 214KB
wgan
images
.gitignore 13B
saved_model
.gitignore 13B
wgan.py 6KB
LICENSE 1KB
assets
keras_gan.png 10KB
bigan
bigan.py 6KB
images
.gitignore 13B
saved_model
.gitignore 13B
context_encoder
images
.gitignore 13B
saved_model
.gitignore 13B
context_encoder.py 9KB
README.md 8KB
aae
images
.gitignore 13B
saved_model
.gitignore 13B
aae.py 6KB
pix2pix
download_dataset.sh 263B
images
.gitignore 13B
saved_model
.gitignore 13B
pix2pix.py 8KB
data_loader.py 2KB
cgan
images
.gitignore 13B
cgan.py 6KB
saved_model
.gitignore 13B
.gitignore 99B
ccgan
images
.gitignore 13B
saved_model
.gitignore 13B
ccgan.py 9KB
wgan_gp
images
.gitignore 13B
saved_model
.gitignore 13B
wgan_gp.py 9KB
discogan
download_dataset.sh 263B
images
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discogan.py 9KB
saved_model
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data_loader.py 2KB
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