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.
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
“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
The cited source says what the claim attributes to it.
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
The cited source says what the claim attributes to it.
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
The cited source says what the claim attributes to it.
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
The cited source says what the claim attributes to it.
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]
The cited source says what the claim attributes to it.
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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
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
“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]
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
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
Sorted by citations
Items 201–300 of 1,057
FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces
Wang, R., Juefei-Xu, F., Ma, L., Xie, X., Huang, Y., Wang, J., & Liu, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/476 · Cited by 313
AI Art and its Impact on Artists
Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., & Gebru, T. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604681 · Cited by 265
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
Zhang, Y., Ling, H., Gao, J., Yin, K., Lafleche, J.-F., Barriuso, A., Torralba, A., & Fidler, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01001 · Cited by 259
StyleSwin: Transformer-based GAN for High-resolution Image Generation
Zhang, B., Gu, S., Zhang, B., Bao, J., Chen, D., Wen, F., Wang, Y., & Guo, B. (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/cvpr52688.2022.01102 · Cited by 288
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyPreprint
Wang, Z., She, Q., & Ward, T. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01529 · Cited by 254
Texture Fields: Learning Texture Representations in Function Space
Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., & Geiger, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00463 · Cited by 265
Adversarial Diffusion Distillation
Sauer, A., Lorenz, D., Blattmann, A., & Rombach, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73016-0_6 · Cited by 617
A Review on Generative Adversarial Networks: Algorithms, Theory, and ApplicationsPreprint
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.06937 · Cited by 252
IMD2020: A Large-Scale Annotated Dataset Tailored for Detecting Manipulated Images
Novozámský, A., Mahdian, B., & Saic, S. (2020) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw50321.2020.9096940 · Cited by 270
Adversarial Latent Autoencoders
Pidhorskyi, S., Adjeroh, D., & Doretto, G. (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/cvpr42600.2020.01411 · Cited by 258
High-Fidelity GAN Inversion for Image Attribute Editing
Wang, T., Zhang, Y., Fan, Y., Wang, J., & Chen, 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/cvpr52688.2022.01109 · Cited by 251
Unsupervised Discovery of Interpretable Directions in the GAN Latent SpacePreprint
Voynov, A. S., & Babenko, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03754 · Cited by 272
Adaptive diffusion priors for accelerated MRI reconstruction
Güngör, A., Dar, S. U. H., Öztürk, Ş., Korkmaz, Y., Bedel, H. A., Elmas, G., Özbey, M., & Çukur, T. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102872 · Cited by 277
Deepfake detection by human crowds, machines, and machine-informed crowds
Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110013119 · Cited by 248
Multistage GAN for Fabric Defect Detection
Liu, J., Wang, C., Su, H., Du, B., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959741 · Cited by 247
StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
Or-El, R., Luo, X., Shan, M., Shechtman, E., Park, J. J., & Kemelmacher-Shlizerman, I. (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/cvpr52688.2022.01314 · Cited by 264
Monocular Expressive Body Regression Through Body-Driven Attention
Choutas, V., Pavlakos, G., Bolkart, T., Tzionas, D., & Black, M. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_2 · Cited by 249
Identification of deep network generated images using disparities in color components
Li, H., Li, B., Tan, S., & Huang, J. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107616 · Cited by 284
Consumer Responses to AI-Generated Charitable Giving Ads
Arango, L., Singaraju, S. P., & Niininen, O. (2023) · Journal of Advertising · doi:10.1080/00913367.2023.2183285 · Cited by 243
Detecting Deep-Fake Videos from Appearance and Behavior
Agarwal, S., Farid, H., El-Gaaly, T., & Lim, S.-N. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs49906.2020.9360904 · Cited by 242
“In the static image domain, highly realistic images of people can now be synthesized using generative adversarial networks (GANs) [2], [3].”
BackgroundEffectively Unbiased FID and Inception Score and Where to Find Them
Chong, M. J., & Forsyth, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00611 · Cited by 242
Artificial intelligence in digital media: the era of deepfakes
Karnouskos, S. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.3001312 · Cited by 239
Learning Spatial Attention for Face Super-Resolution
Chen, C., Gong, D., Wang, H., Li, Z., & Wong, K.-Y. K. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3043093 · Cited by 244
La nueva realidad de la educación ante los avances de la inteligencia artificial generativa
García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2023) · RIED-Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.27.1.37716 · Cited by 238
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., & Furon, T. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02053 · Cited by 251
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
Liu, M.-Y., Huang, X., Yu, J., Wang, T.-C., & Mallya, A. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3049196 · Cited by 236
Denoising Diffusion Models for Plug-and-Play Image Restoration
Zhu, Y., Zhang, K., Liang, J., Cao, J., Wen, B., Timofte, R., & Gool, L. V. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00129 · Cited by 294
Few-shot Image Generation via Cross-domain Correspondence
Ojha, U., Li, Y., Lu, J., Efros, A. A., Lee, Y. J., Shechtman, E., & Zhang, R. (2021) · 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/cvpr46437.2021.01060 · Cited by 256
Deepfake Detection: Current Challenges and Next Steps
Lyu, S. (2020) · 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew46912.2020.9105991 · Cited by 234
Multimodal Neurons in Artificial Neural Networks
Goh, G., Cammarata, N., Voss, C., Carter, S., Petrov, M., Schubert, L., Radford, A., & Olah, C. (2021) · Distill · doi:10.23915/distill.00030 · Cited by 330
Design Principles for Generative AI Applications
Weisz, J. D., He, J., Muller, M., Hoefer, G., Miles, R., & Geyer, W. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642466 · Cited by 232
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Korkmaz, Y., Dar, S. U. H., Yurt, M., Özbey, M., & Çukur, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3147426 · Cited by 232
FateZero: Fusing Attentions for Zero-shot Text-based Video Editing
Qi, C., Cun, X., Zhang, Y., Lei, C., Wang, X., Shan, Y., & Chen, Q. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01460 · Cited by 388
Meta-Sim: Learning to Generate Synthetic DatasetsPreprint
(2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 235
Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges
Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227
Recurrent convolutional structures for audio spoof and video deepfake detection
Chintha, A., Thai, B., Sohrawardi, S. J., Bhatt, K., Hickerson, A., Wright, M., & Ptucha, R. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2999185 · Cited by 226
Peng, X. B., Guo, Y., Halper, L., Levine, S., & Fidler, S. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530110 · Cited by 285
Rethinking FID: Towards a Better Evaluation Metric for Image Generation
Jayasumana, S., Ramalingam, S., Veit, A., Gläsner, D., Chakrabarti, A., & Kumar, S. (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/cvpr52733.2024.00889 · Cited by 320
Swapping Autoencoder for Deep Image ManipulationPreprint
Park, T., Zhu, J.-Y., Wang, O., Lu, J., Shechtman, E., Efros, A. A., & Zhang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00653 · Cited by 263
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
Liang, J., Zeng, H., & Lei Zhang, A. (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/cvpr52688.2022.00557 · Cited by 224
More Control for Free! Image Synthesis with Semantic Diffusion Guidance
Liu, X., Park, D. H., Azadi, S., Zhang, G., Chopikyan, A., Hu, Y., Shi, H., Rohrbach, A., & Darrell, T. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00037 · Cited by 274
Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches
Agarwal, S., Farid, H., Fried, O., & Agrawala, M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00338 · Cited by 234
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413707 · Cited by 220
Implicit identity leakage: The stumbling block to improving deepfake detection generalization
Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., & Ge, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00389 · Cited by 220
Hierarchical Fine-Grained Image Forgery Detection and Localization
Guo, X., Liu, X., Ren, Z., Grosz, S. A., Masi, I., & Liu, X. (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/cvpr52729.2023.00308 · Cited by 251
Xiao-min, Z., Li, C., Rahaman, M. M., Yao, Y., Ai, S., Sun, C., Wang, Q., Zhang, Y., Li, M., Li, X., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2993788 · Cited by 228
Leinonen, J., Nerini, D., & Berne, A. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3032790 · Cited by 253
Hong, F., Zhang, M., Pan, L., Cai, Z., Yang, L., & Liu, Z. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530094 · Cited by 335
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
Rakotonirina, N. C., & Rasoanaivo, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054071 · Cited by 215
GAN Compression: Efficient Architectures for Interactive Conditional GANs
Li, M., Lin, J., Ding, Y., Liu, Z., Zhu, J.-Y., & Han, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00533 · Cited by 216
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
Wang, Z., Wang, Z., Yu, Z., Deng, W., Li, J., Gao, T., & Wang, Z. (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/cvpr52688.2022.00409 · Cited by 216
Dash, A., Ye, J., & Wang, G. (2023) · IEEE Access · doi:10.1109/access.2023.3346273 · Cited by 251
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Wu, R., Yang, T., Sun, L., Zhang, Z., Li, S., & Zhang, L. (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/cvpr52733.2024.02405 · Cited by 279
Deepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214
“Deepfake technology has made it possible to make these videos look real; therefore, it is necessary to assess the videos’ authenticity (Westerlund 2019; Karras et al. 2019).”
Improved Techniques for Training Single-Image GANs
Hinz, T., Fisher, M., Wang, O., & Wermter, S. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00134 · Cited by 229
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
Chen, H., Wang, Y., Lagadec, B., Dantcheva, A., & Brémond, F. (2021) · 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/cvpr46437.2021.00204 · Cited by 220
Yolo V4 for Advanced Traffic Sign Recognition With Synthetic Training Data Generated by Various GAN
Dewi, C., Chen, R.-C., Liu, Y., Jiang, X., & Hartomo, K. D. (2021) · IEEE Access · doi:10.1109/access.2021.3094201 · Cited by 224
Are GAN Generated Images Easy to Detect? A Critical Analysis of the State-Of-The-Art
Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., & Verdoliva, L. (2021) · 2021 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428429 · Cited by 212
Deep Single-Image Portrait Relighting
Zhou, H., Hadap, S., Sunkavalli, K., & Jacobs, D. W. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00729 · Cited by 222
Counterfactual Zero-Shot and Open-Set Visual Recognition
Yue, Z., Wang, T., Sun, Q., Hua, X., & Zhang, H. (2021) · 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/cvpr46437.2021.01515 · Cited by 221
RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
Wang, T., Zhang, B., Zhang, T., Gu, S., Bao, J., Baltrušaitis, T., Shen, J., Chen, D., Wen, F., Chen, Q., et al. (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/cvpr52729.2023.00443 · Cited by 258
Adversarial text-to-image synthesis: A review
Frolov, S., Hinz, T., Raue, F., Hees, J., & Dengel, A. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.019 · Cited by 228
Advanced metaheuristic optimization techniques in applications of deep neural networks: a review
Elaziz, M. A., Dahou, A., Abualigah, L., Yu, L., Alshinwan, M., Khasawneh, A. M., & Lu, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05960-5 · Cited by 226
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Pan, X., Tewari, A., Leimkühler, T., Liu, L., Meka, A., & Theobalt, C. (2023) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3588432.3591500 · Cited by 264
StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models
Wang, Z., Zhao, L., & Xing, W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00706 · Cited by 229
Few-shot Video-to-Video SynthesisPreprint
Wang, T.-C., Liu, M.-Y., Tao, A., Liu, G., Kautz, J., & Catanzaro, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12713 · Cited by 269
Semantic photo manipulation with a generative image prior
Bau, D., Strobelt, H., Peebles, W., Wulff, J., Zhou, B., Zhu, J.-Y., & Torralba, A. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323023 · Cited by 207
Progressive Domain Expansion Network for Single Domain Generalization
Li, L., Gao, K., Cao, J., Huang, Z., Weng, Y., Mi, X., Yu, Z., Li, X., & Xia, B. (2021) · 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/cvpr46437.2021.00029 · Cited by 215
HeadNeRF: A Realtime NeRF-based Parametric Head Model
Hong, Y., Peng, B., Xiao, H., Liu, L., & Zhang, J. (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/cvpr52688.2022.01973 · Cited by 207
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation
Deng, Y., Yang, J., Xiang, J., & Tong, X. (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/cvpr52688.2022.01041 · Cited by 215
Consistency Regularization for Generative Adversarial NetworksPreprint
Zhang, H., Zhang, Z., Odena, A., & Lee, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12027 · Cited by 218
PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering
Ren, Y., Li, G., Chen, Y., Li, T. H., & Liu, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01350 · Cited by 224
AI + Ethics Curricula for Middle School Youth: Lessons Learned from Three Project-Based Curricula
Williams, R., Ali, S., Devasia, N., DiPaola, D., Hong, J., Kaputsos, S. P., Jordan, B., & Breazeal, C. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00298-y · Cited by 263
Understanding cities with machine eyes: A review of deep computer vision in urban analytics
Ibrahim, M. R., Haworth, J., & Cheng, T. (2019) · Cities · doi:10.1016/j.cities.2019.102481 · Cited by 212
Perception Prioritized Training of Diffusion Models
Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., & Yoon, S. (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/cvpr52688.2022.01118 · Cited by 238
Blind Face Restoration via Deep Multi-scale Component Dictionaries
Li, X., Chen, C., Zhou, S., Lin, X., Zuo, W., & Lei Zhang, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_23 · Cited by 202
A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning
Gu, Y., Chi, J., Liu, J., Yang, L., Zhang, B., Yu, D., Zhao, Y., & Lu, X. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104806 · Cited by 212
Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
Tan, C., Zhao, Y., Wei, S., Gu, G., & Wei, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01165 · Cited by 203
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection
Zhang, G., Cui, K., Hung, T.-Y., & Lu, S. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00257 · Cited by 200
ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
Vitoria, P., Raad, L., & Ballester, C. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093389 · Cited by 204
Unpaired motion style transfer from video to animation
Aberman, K., Weng, Y., Lischinski, D., Cohen‐Or, D., & Chen, B. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392469 · Cited by 200
Time-Dependent Deep Image Prior for Dynamic MRI
Yoo, J., Jin, K. H., Gupta, H., Yerly, J., Stuber, M., & Unser, M. A. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3084288 · Cited by 200
StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
Skorokhodov, I., Tulyakov, S., & Elhoseiny, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00361 · Cited by 199
A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks
Wang, L., Chen, W., Yang, W., Bi, F., & Yu, F. R. (2020) · IEEE Access · doi:10.1109/access.2020.2982224 · Cited by 210
SeedFormer: Patch Seeds Based Point Cloud Completion with Upsample Transformer
Zhou, H., Cao, Y., Chu, W., Zhu, J., Lü, T., Tai, Y. Y., & Wang, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20062-5_24 · Cited by 204
OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder
Khalid, H., & Woo, S. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00336 · Cited by 197
XingGAN for Person Image Generation
Tang, H., Bai, S., Zhang, L., Torr, P. H. S., & Sebe, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_43 · Cited by 199
Towards Language-Free Training for Text-to-Image Generation
Zhou, Y., Zhang, R., Chen, C., Li, C., Tensmeyer, C., Yu, T., Gu, J., Xu, J., & Sun, T. (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/cvpr52688.2022.01738 · Cited by 292
Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., & Wei, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28310 · Cited by 215
Challenges for Higher Education in the Era of Widespread Access to Generative AI
Walczak, K., & Cellary, W. (2023) · Economics and Business Review · doi:10.18559/ebr.2023.2.743 · Cited by 196
Human Motion Prediction via Spatio-Temporal Inpainting
Hernandez, A., Gall, J., & Moreno, F. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00723 · Cited by 196
Text to Image Generation with Semantic-Spatial Aware GAN
Liao, W., Hu, K., Yang, M. Y., & Rosenhahn, B. (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/cvpr52688.2022.01765 · Cited by 208
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191
“(Liu et al., 2015) 10,000 200,000 20 VGGFace2 (Cao et al., 2018) 9,131 3,310,000 362.50 FFHQ (Karras et al., 2019) unknown 70,000 unknown VGGFace (Parkhi et al., 2015) 2,622 2,600,000 991.61 Ms-Celeb-1M (Guo et al., 2016) 100,000 10,000,000 100 MegaFace (Kemelmacher-Shlizerman et al., 2016) 690,572 4,700,000 6.81 LSUN…”
BackgroundMethodInfluentialAdaptIS: Adaptive Instance Selection Network
Sofiiuk, K., Barinova, O., & Konushin, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00745 · Cited by 191
Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis
Yang, C., Shen, Y., & Zhou, B. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01429-5 · Cited by 200
Kim, I., Ki, C.-W. C., Lee, H., & Kim, Y. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114611 · Cited by 230
The Rocketbox Library and the Utility of Freely Available Rigged Avatars
González-Franco, M., Ofek, E., Pan, Y., Antley, A., Steed, A. J., Spanlang, B., Maselli, A., Banakou, D., Pelechano, N., Orts‐Escolano, S., et al. (2020) · Frontiers in Virtual Reality · doi:10.3389/frvir.2020.561558 · Cited by 203
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Chen, C., Li, X., Yang, L., Lin, X., Lei Zhang, A., & Wong, K.-Y. K. (2021) · 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/cvpr46437.2021.01172 · Cited by 190
A Survey of AI-Generated Content (AIGC)
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2024) · ACM Computing Surveys · doi:10.1145/3704262 · Cited by 1,457
One Shot Face Swapping on Megapixels
Zhu, Y., Li, Q., Wang, J., Xu, C., & Sun, Z. (2021) · 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/cvpr46437.2021.00480 · Cited by 189
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.
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
