PGGAN

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Abstract

We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at $1024^{2}$. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.

Results and models

Models	Details	MS-SSIM	SWD(xx,xx,xx,xx/avg)	Config	Download
pggan_128x128	celeba-cropped	0.3023	3.42, 4.04, 4.78, 20.38/8.15	config	model
pggan_128x128	lsun-bedroom	0.0602	3.5, 2.96, 2.76, 9.65/4.72	config	model
pggan_1024x1024	celeba-hq	0.3379	8.93, 3.98, 3.07, 2.64/4.655	config	model

Citation

PGGAN (arXiv'2017)

@article{karras2017progressive,
  title={Progressive growing of gans for improved quality, stability, and variation},
  author={Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},
  journal={arXiv preprint arXiv:1710.10196},
  year={2017},
  url={https://arxiv.org/abs/1710.10196},
}