Structured GANs

WACV 2018 pp. 719-728

doi:10.1109/WACV.2018.00084 /wacv/2018/peleg2018wacv-structured/

Abstract

We present Generative Adversarial Networks (GANs), in which the symmetric property of the generated images is controlled. This is obtained through the generator network's architecture, while the training procedure and the loss remain the same. The symmetric GANs are applied to face image synthesis in order to generate novel faces with a varying amount of symmetry. We also present an unsupervised face rotation capability, which is based on the novel notion of one-shot fine tuning.

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Cite

Text

Peleg and Wolf. "Structured GANs." IEEE/CVF Winter Conference on Applications of Computer Vision, 2018. doi:10.1109/WACV.2018.00084

Markdown

[Peleg and Wolf. "Structured GANs." IEEE/CVF Winter Conference on Applications of Computer Vision, 2018.](https://mlanthology.org/wacv/2018/peleg2018wacv-structured/) doi:10.1109/WACV.2018.00084

BibTeX

@inproceedings{peleg2018wacv-structured,
  title     = {{Structured GANs}},
  author    = {Peleg, Irad and Wolf, Lior},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2018},
  pages     = {719-728},
  doi       = {10.1109/WACV.2018.00084},
  url       = {https://mlanthology.org/wacv/2018/peleg2018wacv-structured/}
}