Cited by 6,332Generative Adversarial Networks and Image SynthesisDigital Media Forensic DetectionAdvanced Neural Network Applications

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Analyzing and Improving the Image Quality of StyleGAN

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Karras, Tero, Laine, Samuli, Aittala, Miika, Hellsten, Janne, Lehtinen, Jaakko, Aila, Timo·2020·2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr42600.2020.00813

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Abstract

The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

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Authors: Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T.
Affiliations: Nvidia (United Kingdom) (GB)
Aalto University (FI)
ORCID: Laine, Samuli
Aittala, Miika
Lehtinen, Jaakko
Aila, Timo
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2020-06
Pages: 8107-8116
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr42600.2020.00813
OpenAlex: W2995327724
Semantic Scholar: 14fdc18d9c164e5b0d6d946b3238c04e81921358
Cited by: 6,332
References: 55
Citations by year: 2026: 463 · 2025: 1070 · 2024: 1389 · 2023: 1362 · 2022: 1009 · 2021: 792 · 2020: 243
FWCI: 262.37
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
TIB Data Manager
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: StyleGAN, image quality, unconditional image generation, model capacity

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Analyzing and Improving the Image Quality of StyleGAN

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