First published: 2017/01/26 (7 years ago) Abstract: We introduce a new algorithm named WGAN, an alternative to traditional GAN
training. In this new model, we show that we can improve the stability of
learning, get rid of problems like mode collapse, and provide meaningful
learning curves useful for debugging and hyperparameter searches. Furthermore,
we show that the corresponding optimization problem is sound, and provide
extensive theoretical work highlighting the deep connections to other distances
between distributions.
_Objective:_ Robust unsupervised learning of a probability distribution using a new module called the `critic` and the `Earth-mover distance`.
_Dataset:_ [LSUN-Bedrooms](http://lsun.cs.princeton.edu/2016/)
## Inner working:
Basically train a `critic` until convergence to retrieve the Wasserstein-1 distance, see pseudo-algorithm below:
[](https://cloud.githubusercontent.com/assets/17261080/25667162/003c9330-3023-11e7-9081-c181011f4e6f.png)
## Results:
* Easier training: no need for batch normalization and no need to fine-tune generator/discriminator balance.
* Less sensitivity to network architecture.
* Very good proxy that correlates very well with sample quality.
* Non-vanishing gradients.
