ML Anthology

Sphere Generative Adversarial Network Based on Geometric Moment Matching

Sung Woo Park, Junseok Kwon
CVPR 2019
doi:10.1109/CVPR.2019.00442 /cvpr/2019/park2019cvpr-sphere/

Abstract

We propose sphere generative adversarial network (GAN), a novel integral probability metric (IPM)-based GAN. Sphere GAN uses the hypersphere to bound IPMs in the objective function. Thus, it can be trained stably. On the hypersphere, sphere GAN exploits the information of higher-order statistics of data using geometric moment matching, thereby providing more accurate results. In the paper, we mathematically prove the good properties of sphere GAN. In experiments, sphere GAN quantitatively and qualitatively surpasses recent state-of-the-art GANs for unsupervised image generation problems with the CIFAR-10, STL-10, and LSUN bedroom datasets. Source code is available at https://github.com/pswkiki/SphereGAN.

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Cite

Text

Park and Kwon. "Sphere Generative Adversarial Network Based on Geometric Moment Matching." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00442

Markdown

[Park and Kwon. "Sphere Generative Adversarial Network Based on Geometric Moment Matching." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/park2019cvpr-sphere/) doi:10.1109/CVPR.2019.00442

BibTeX

@inproceedings{park2019cvpr-sphere,
  title     = {{Sphere Generative Adversarial Network Based on Geometric Moment Matching}},
  author    = {Park, Sung Woo and Kwon, Junseok},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00442},
  url       = {https://mlanthology.org/cvpr/2019/park2019cvpr-sphere/}
}