The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
A Style-Based Generator Architecture for Generative Adversarial Networks
Open accessAbstract
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces.
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“It has been demonstrated that GANbased synthesis approaches may create unexpectedly high-quality false videos (Karras et al. 2019).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Karras et al. 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“100K-Generated-Images (Karras et al., 2019): This dataset includes 100,000 synthesised face, bedroom, car and cat images by a GAN generator trained based on real images in the FFHQ28 and LSUN29 datasets”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Karras et al., 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The generator learns to produce increasingly realistic images through this adversarial process, creating convincing deepfakes (Karras, Laine & Aila, 2019).”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Karras, Laine & Aila, 2019
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“StyleGAN (Karras et al. 2019) has proposed a new design to automatically learn the unsupervised separation of high-level attributes such as pose and human identity.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Karras et al. 2019
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In the citing paper
“The StyleGAN is able to generate high-resolution images with higher image quality and wider detailed variations.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Karras et al. 2019
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In the citing paper
“It contains 70,000 high-quality images at 1024 × 1024 resolution.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Karras et al. 2019
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In the citing paper
“iFakeFaceDB (Neves et al. 2020) is a fake image dataset for the study of synthetic face manipulation detection. It contains about 87,000 synthetic face images generated by the StyleGAN model (Karras et al. 2019) and transformed with the GANprintR (Neves et al. 2020) approach.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Karras et al. 2019
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In the citing paper
“In this paper, the five most famous and effective architectures in state-of-the-art for face Deepfakes were taken into account: STARGAN [6], STYLEGAN [19], STYLEGAN2 [20], ATTGAN [15] and GDWCT [5].”
From DeepFake Detection by Analyzing Convolutional Traces (2020) · cites this paper as [19]
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In the citing paper
“Hsu et al. [85] introduced a two-phase deep learn- ing method for detection of deepfake images.”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [85]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Discrim- inative features between the fake and real images are extracted through the CFFN learning process based on the use of pairwise information, which is the label of each pair of two input images.”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [85]
Experimental — accuracy not yet measuredIn the citing paper
“Models such as StyleGAN [79], GPT [20, 130, 137, 138], and Codex [28] have demonstrated that powerful generative models can produce works at a human-like level of fidelity.”
From Design Principles for Generative AI Applications (2024) · cites this paper as [79]
In the citing paper
“Generative AI technologies have reached an inflection point in consumer adoption and enterprise value, sparked by technological advancements in machine learning architectures such as GANs [56, 79], VAEs [86], and transformers [38, 170].”
From Design Principles for Generative AI Applications (2024) · cites this paper as [79]
In the citing paper
“On first glance, deepfakes and other algorithmically generated images of people (e.g., images generated by StyleGAN) look quite realistic (35).”
From Deepfake detection by human crowds, machines, and machine-informed crowds (2022) · cites this paper as [35]
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Citations
Cites
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384
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Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
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
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365
Research works printed but not linked: 2
- doan, 2018, Online adaptative curriculum learning for GANs, CoRR
- durugkar, 2016, Generative multiadversarial networks, CoRR
Cited by
Sorted by citations
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 20,302
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332
Self-citationAdding Conditional Control to Text-to-Image Diffusion Models
Zhang, L., Rao, A., & Agrawala, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00355 · Cited by 4,547
Metadata
- Authors
- Karras, T., Laine, S., & Aila, T.
- Affiliations
- Nvidia (United Kingdom) (GB)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Volume
- 43
- Issue
- 12
- Pages
- 4396-4405
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2904367110
- Semantic Scholar
- ceb2ebef0b41e31c1a21b28c2734123900c005e2
- Cited by
- 11,714
- References
- 75
- Citations by year
- 2026: 922 · 2025: 1949 · 2024: 2294 · 2023: 2157 · 2022: 1594 · 2021: 1550 · 2020: 920 · 2019: 319
- FWCI
- 357.39
- 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)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (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 › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- generative adversarial networks, generator architecture, style transfer, disentangled representations, face synthesis
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- The paper
- Karras, T., Laine, S., & Aila, T. (2019). A Style-Based Generator Architecture for Generative Adversarial Networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00453
