The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Analyzing and Improving the Image Quality of StyleGAN
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“StyleGAN2 is also helping detect image deepfakes as it can see if the picture is generated by a network or not (Karras et al., 2020).”
From Audio deepfakes: A survey (2023) · cites this paper as Karras et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“StyleGAN2 (Karras et al. 2020) has exposed several typical artifacts of StyleGAN and has proposed changes in both model architecture and training methods to address them.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Karras et al. 2020
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“LayerNorm) (Lei Ba et al., 2016) instead of Group Normalization (GroupNorm) (Wu and He, 2018), which prevents large magnitudes from appearing in specific regions of the latent space or reconstructed outputs (Karras et al., 2020; Sadat et al., 2024).”
From Cosmos World Foundation Model Platform for Physical AI (2025) · cites this paper as Karras et al., 2020
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
The Unreasonable Effectiveness of Deep Features as a Perceptual MetricPreprint
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1801.03924 · Cited by 21,362
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
Flagged first, then by citations
Research works printed but not linked: 3
- nikitko, 2019, StyleGAN - Encoder for official Ten-sorFlow implementation
- bergstrom, 0, Which face is real?
- zhang, 0, Making convolutional networks shift-invariant again, Proc ICML
Cited by
Sorted by citations
Items 101–107 of 107
Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency
Hyun, S., Kim, J., & Heo, J.-P. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01068 · Cited by 16
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
Yuan, X., Baek, J., Xu, K., Tov, O., & Fei, H. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00059 · Cited by 14
Cosmos World Foundation Model Platform for Physical AI
NVIDIA, :, Agarwal, N., Ali, A., Bala, M., Balaji, Y., Barker, E., Cai, T., Chattopadhyay, P., Chen, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.03575 · Cited by 13
Pontorno, O., Guarnera, L., & Battiato, S. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-78305-0_12 · Cited by 6
Anymate: A Dataset and Baselines for Learning 3D Object Rigging
Deng, Y., Zhang, Y., Geng, C., Wu, S., & Wu, J. (2025) · Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers · doi:10.1145/3721238.3730743 · Cited by 6
Chen, Z., Lu, L., Yuan, Z., Zhu, Y., Li, Y., Yuan, C., & Deng, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27889 · Cited by 3
Han, Y.-H., Huang, T.-M., Hua, K.-L., & Chen, J.-C. (2024) · arXiv · doi:10.48550/arxiv.2404.05583 · Cited by 45
Metadata
- Authors
- Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T.
- Affiliations
- Nvidia (United Kingdom) (GB)
- Aalto University (FI)
- 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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
