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

ICLR 2018 Tero KarrasTimo AilaSamuli LaineJaakko Lehtinen

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... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Image Generation CelebA PGGAN FID 7.30 # 2
Image Generation CelebA-HQ 1024x1024 PGGAN FID 7.30 # 3
Image Generation CIFAR-10 PGGAN Inception score 8.80 # 2
Image Generation FFHQ PGGAN FID 8.04 # 3
Image Generation LSUN Bedroom 256 x 256 PGGAN FID 8.34 # 3