# Simple Tensorflow implementation of RelativisticGAN

## RelativisticGAN-Tensorflow

Simple Tensorflow implementation of RelativisticGAN

## Issue

- For 256x256, the network does not generate the image properly. (DCGAN Architecture)
- I think,
`RaDRAGAN`

more better than`RaLSGAN`

## Usage

### dataset

```
> python download.py celebA
```

`mnist`

and`cifar10`

are used inside keras- For
`your dataset`

, put images like this:

```
├── dataset
└── YOUR_DATASET_NAME
├── xxx.jpg (name, format doesn't matter)
├── yyy.png
└── ...
```

### train

- python main.py --phase train --dataset celebA --Ra True --gan_type dragan

### test

- python main.py --phase test --dataset celebA --Ra True --gan_type dragan

## Summary

**"the discriminator estimates the probability that the given real data is more realistic than a randomly sampled fake data"**

*= RGAN*

**"the discriminator estimates the probability that the given real data is more realistic than fake data, on average"**

*= RaGAN*

### Idea

### Formulation

Name |
Formulation |
---|---|

GAN |

**RGAN**|

**RaGAN**|

**RaGAN-GP**|

**RaLSGAN**|

**RaHingeGAN**|

## Results

- 128x128 celebA
- 200k iterations (but, 100k iteration is also enough)
**RaDRAGAN**is not in the paper, I just tried because I wanted to do it.

Name |
Original |
Original + Ra |
---|---|---|

GAN |

**LSGAN** | | |

**HingeGAN** | Will be soon | |

**DRAGAN** | | |

## Error

### Original DRAGAN

- In the case of
`DRAGAN`

, the images are sometimes distorted during the training

## Author

Junho Kim

## GitHub

### Comments

### Subscribe to Python Awesome

Get the latest posts delivered right to your inbox