## SeFa - Closed-Form Factorization of Latent Semantics in GANs

**Figure:** *Versatile semantics found from various types of GAN models using SeFa.*

Closed-Form Factorization of Latent Semantics in GANs

Yujun Shen, Bolei Zhou

arXiv preprint arXiv:2007.06600

[Paper] [Project Page] [Demo]

In this repository, we propose a *closed-form* approach, termed as **SeFa**, for *unsupervised* latent semantic factorization in GANs. With this algorithm, we are able to discover versatile semantics from different GAN models trained on various datasets. Most importantly, the proposed method does *not* rely on pre-trained semantic predictors and has an extremely *fast* implementation (*i.e.*, less than 1 second to interpret a model). Below show some interesting results on anime faces, cats, and cars.

**NOTE:** The following semantics are identified in a completely *unsupervised* manner, and post-annotated for reference.

Anime Faces | ||
---|---|---|

Pose | Mouth | Painting Style |

Cats | ||
---|---|---|

Posture (Left & Right) | Posture (Up & Down) | Zoom |

Cars | ||
---|---|---|

Orientation | Vertical Position | Shape |

## BibTeX

```
@article{shen2020closedform,
title = {Closed-Form Factorization of Latent Semantics in GANs},
author = {Shen, Yujun and Zhou, Bolei},
journal = {arXiv preprint arXiv:2007.06600},
year = {2020}
}
```