Progressive Growing of GANs for Improved Quality, Stability, and Variation
@article{Karras2018ProgressiveGO, title={Progressive Growing of GANs for Improved Quality, Stability, and Variation}, author={Tero Karras and Timo Aila and S. Laine and J. Lehtinen}, journal={ArXiv}, year={2018}, volume={abs/1710.10196} }
We describe a new training methodology for generative adversarial networks. [...] Key Method 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…Expand Abstract
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