2017-01-30 14:29:17 +00:00
Wasserstein GAN
===============
Code accompanying the paper ["Wasserstein GAN" ](https://arxiv.org/abs/1701.07875 )
##Prerequisites
- Computer with Linux or OSX
- [PyTorch ](http://pytorch.org )
- For training, an NVIDIA GPU is strongly recommended for speed. CPU is supported but training is very slow.
2017-01-30 14:41:11 +00:00
Two main empirical claims:
###Generator sample quality correlates with discriminator loss
![gensample ](imgs/w_combined.png "sample quality correlates with discriminator loss" )
###Improved model stability
![stability ](imgs/compare_dcgan.png "stability" )
2017-01-30 14:29:17 +00:00
##Reproducing LSUN experiments
**With DCGAN:**
2017-01-30 14:41:11 +00:00
```bash
2017-01-30 14:29:17 +00:00
python main.py --dataset lsun --dataroot [lsun-train-folder] --cuda
```
**With MLP:**
2017-01-30 14:41:11 +00:00
```bash
2017-01-30 14:29:17 +00:00
python main.py --mlp_G --ngf 512
```
2017-01-30 14:41:11 +00:00
Generated samples will be in the `samples` folder.
If you plot the value `-Loss_D` , then you can reproduce the curves from the paper. The curves from the paper (as mentioned in the paper) have a median filter applied to them:
```python
med_filtered_loss = scipy.signal.medfilt(-Loss_D, dtype='float64'), 101)
```
2017-01-30 14:29:17 +00:00
More improved README in the works.