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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.
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“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.
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“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
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“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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“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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“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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“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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“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]
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“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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“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]
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“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
“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
(2014) · Cited by 33,419
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
TensorFlow: A system for large-scale machine learningPreprint
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
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Items 801–900 of 1,057
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Feng, Y., Feng, H., Black, M. J., & Bolkart, T. (2021) · ACM Transactions on Graphics · doi:10.1145/3476576.3476646 · Cited by 752
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Skorokhodov, I., Sotnikov, G., & Elhoseiny, M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01388 · Cited by 91
Valle-Pérez, G., Henter, G. E., Beskow, J., Holzapfel, A., Oudeyer, P., & Alexanderson, S. (2021) · ACM Transactions on Graphics · doi:10.1145/3478513.3480570 · Cited by 89
CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions
Abdal, R., Zhu, P., Femiani, J. C., Mitra, N. J., & Wonka, P. (2022) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3528233.3530747 · Cited by 89
Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods
Abir, W. H., Khanam, F. R., Alam, K. N., Hadjouni, M., Elmannai, H., Bourouis, S., Dey, R., & Khan, M. M. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.029653 · Cited by 97
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Li, J., Pei, Z., Li, W., Gao, G., Wang, L., Wang, Y., & Zeng, T. (2024) · ACM Computing Surveys · doi:10.1145/3659100 · Cited by 100
GAN Memory with No ForgettingPreprint
Cong, Y., Zhao, M., Li, J., Wang, S., & Carin, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07543 · Cited by 77
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited DataPreprint
Jiang, L., Dai, B., Wu, W., & Loy, C. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06849 · Cited by 79
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More Real Than Real: A Study on Human Visual Perception of Synthetic Faces [Applications Corner]
Lago, F., Pasquini, C., Böhme, R., Dumont, H., Goffaux, V., & Boato, G. (2021) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3120982 · Cited by 91
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Li, M., Zhou, G., Chen, A., Yi, J., Lü, C., He, M., & Hu, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106779 · Cited by 77
Sun, Z., Zhang, H., Bai, J., Liu, M., & Hu, Z. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109157 · Cited by 82
Yang, S., Jiang, L., Liu, Z., & Loy, C. C. (2022) · ACM Transactions on Graphics · doi:10.1145/3550454.3555437 · Cited by 77
InOut: Diverse Image Outpainting via GAN Inversion
Cheng, Y.-C., Lin, C. H., Lee, H.-Y., Ren, J., Tulyakov, S., & Yang, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01114 · Cited by 77
Image neural style transfer: A review
Cai, Q., Ma, M., Wang, C., & Li, H. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108723 · Cited by 77
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Wang, H., Song, K., Fan, J., Wang, Y., Xie, J., & Zhang, Z. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01000 · Cited by 83
Online Detection of AI-Generated Images
Epstein, D. C., Jain, I., Wang, O., & Zhang, R. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00045 · Cited by 91
Painting with baryons: augmenting N-body simulations with gas using deep generative models
Tröster, T., Ferguson, C., Harnois-Déraps, J., & McCarthy, I. G. (2019) · Monthly Notices of the Royal Astronomical Society: Letters · doi:10.1093/mnrasl/slz075 · Cited by 77
Perceptual Adversarial Robustness: Defense Against Unseen Threat ModelsPreprint
Laidlaw, C., Singla, S., & Feizi, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12655 · Cited by 126
On Leveraging Pretrained GANs for Generation with Limited DataPreprint
Zhao, M., Cong, Y., & Carin, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11810 · Cited by 87
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Hou, Y., Guo, Q., Huang, Y., Xie, X., Ma, L., & Zhao, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01181 · Cited by 76
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
Lin, J., Pang, Y., Xia, Y., Chen, Z., & Luo, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58548-8_2 · Cited by 77
A systematic survey of face stimuli used in psychological research 2000–2020
Dawel, A., Miller, E. J., Horsburgh, A., & Ford, P. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01705-3 · Cited by 76
Prokopiou, P. C., Engels, N., Papp, K. V., Scott, M. R., Schultz, A. P., Schneider, C., Farrell, M. E., Buckley, R. F., Quiroz, Y. T., Fakhri, G. E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28986-2 · Cited by 81
Prezja, F., Paloneva, J., Pölönen, I., Niinimäki, E., & Äyrämö, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-23081-4 · Cited by 79
Chen, Z., Wang, G., & Liu, Z. (2022) · ACM Transactions on Graphics · doi:10.1145/3550454.3555447 · Cited by 92
A Closer Look at Few-shot Image Generation
Zhao, Y., Ding, H., Huang, H., & Cheung, N. M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00893 · Cited by 80
Guo, Z., Jia, Z., Wang, L., Wang, D., Yang, G., & Kasabov, N. K. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324739 · Cited by 76
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
Li, Y., Singh, K. K., Ojha, U., & Lee, Y. J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00806 · Cited by 75
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Yang, Z., Chen, Y., Le, Z., & Ma, Y. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05387-4 · Cited by 75
Park, H., Na, M., Kim, B., Park, S., Kim, K. H., Chang, S., & Ye, J. C. (2022) · Nature Communications · doi:10.1038/s41467-022-30949-6 · Cited by 75
Gradient Normalization for Generative Adversarial Networks
Wu, Y.-L., Shuai, H.-H., Tam, Z. R., & Chiu, H.-Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00631 · Cited by 75
Stitch it in Time: GAN-Based Facial Editing of Real Videos
Tzaban, R., Mokady, R., Gal, R., Bermano, A. H., & Cohen‐Or, D. (2022) · SIGGRAPH Asia Conference Papers · doi:10.1145/3550469.3555382 · Cited by 77
BigColor: Colorization Using a Generative Color Prior for Natural Images
Kim, G., Kang, K., Kim, S., Lee, H., Kim, S., Kim, J., Baek, S., & Cho, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20071-7_21 · Cited by 75
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment
Xiao, J., Li, L., Wang, C., Zha, Z.-J., & Huang, Q. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01092 · Cited by 75
Microstructure reconstruction using diffusion-based generative models
Lee, K., & Yun, G. J. (2023) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2023.2198528 · Cited by 82
ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors
Cao, C., Dong, Q., & Fu, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3280222 · Cited by 80
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Dolezal, J. M., Wolk, R., Hieromnimon, H. M., Howard, F. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S. E., Kwon, J. W., Agni, M., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00399-4 · Cited by 75
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Liu, L., Muelly, M. C., Deng, J., Pfister, T., & Li, L.-J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00617 · Cited by 75
Human detection of political speech deepfakes across transcripts, audio, and video
Groh, M., Sankaranarayanan, A., Singh, N., Kim, D. Y., Lippman, A., & Picard, R. (2024) · Nature Communications · doi:10.1038/s41467-024-51998-z · Cited by 89
Wang, S.-Y., Bau, D., & Zhu, J.-Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01379 · Cited by 75
Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
Smith, M. J., & Geach, J. E. (2023) · Royal Society Open Science · doi:10.1098/rsos.221454 · Cited by 91
Attribute-Preserving Face Dataset Anonymization via Latent Code Optimization
Barattin, S., Tzelepis, C., Patras, I., & Sebe, N. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00773 · Cited by 74
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
Geng, Z., Yang, B., Hang, T., Li, C., Gu, S., Zhang, T., Bao, J., Zhang, Z., Li, H., Hu, H., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01208 · Cited by 133
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Li, Y., Wang, X., Zeng, R., Donta, P. K., Murturi, I., Huang, M., & Dustdar, S. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3596173 · Cited by 102
Frühstück, A., Alhashim, I., & Wonka, P. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322993 · Cited by 78
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Jolicoeur‐Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., & Mitliagkas, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.14080 · Cited by 249
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Zhou, P., Xie, L., Ni, B., & Tian, Q. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.09788 · Cited by 74
Tan, Z., Chai, M., Chen, D., Liao, J., Chu, Q., Yuan, L., Tulyakov, S., & Yu, N. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392488 · Cited by 73
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Devaranjan, J., Kar, A., & Fidler, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_42 · Cited by 73
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Wang, H., Gui, S., Yang, H., Liu, J., & Wang, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58548-8_4 · Cited by 73
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Feng, Y., Huang, X., & Sester, M. (2022) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2022.2048835 · Cited by 79
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Zhang, J., Lin, L., & Liu, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25451 · Cited by 71
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Ennaji, S., Gaspari, F. D., Hitaj, D., Bidi, A. K., & Mancini, L. V. (2025) · IEEE Access · doi:10.1109/access.2025.3600984 · Cited by 106
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Li, B., Wu, F., Weinberger, K. Q., & Belongie, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04312 · Cited by 74
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Ding, K., Zhou, M., Wang, H., Gevaert, O., Metaxas, D., & Zhang, S. (2023) · Scientific Data · doi:10.1038/s41597-023-02125-y · Cited by 71
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Yang, K., & Menczer, F. (2024) · Journal of Quantitative Description: Digital Media · doi:10.51685/jqd.2024.icwsm.7 · Cited by 71
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Gu, X., Luo, W., Ryoo, M. S., & Lee, Y. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58592-1_43 · Cited by 70
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Chen, B., Ju, X., Xiao, B., Ding, W., Zheng, Y., & de Albuquerque, V. H. C. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.006 · Cited by 71
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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
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.
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