mlsanityData to 14 Oct 2026

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

arXiv 1710.10196 ↗First posted 27 Oct 2017cs.NE cs.LG stat.MLcited by 2,988

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Cited by 654 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 2,988 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Oct 2017.

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Citing papers per year: 2018: 3; 2019: 1; 2020: 1; 2021: 588; 2022: 536; 2023: 625; 2024: 500; 2025: 446; 2026: 288.

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