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 101–200 of 1,057
Towards Universal Fake Image Detectors that Generalize Across Generative Models
Ojha, U., Li, Y., & Lee, Y. J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02345 · Cited by 428
SynSin: End-to-End View Synthesis From a Single Image
Wiles, O., Gkioxari, G., Szeliski, R., & Johnson, 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/cvpr42600.2020.00749 · Cited by 418
The deepfake detection challenge (dfdc) dataset
Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., & Ferrer, C. C. (2020) · arXiv · doi:10.48550/arxiv.2006.07397 · Cited by 427
Survey on Synthetic Data Generation, Evaluation Methods and GANs
Figueira, Á., & Vaz, B. (2022) · Mathematics · doi:10.3390/math10152733 · Cited by 493
SimSwap: An Efficient Framework For High Fidelity Face Swapping
Chen, R., Chen, X., Ni, B., & Ge, Y. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413630 · Cited by 418
Fine-Grained Visual Classification via Progressive Multi-granularity Training of Jigsaw Patches
Du, R., Chang, D., Bhunia, A. K., Xie, J., Ma, Z., Song, Y.-Z., & Guo, J. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_10 · Cited by 430
Scaling up GANs for Text-to-Image Synthesis
Kang, M., Zhu, J.-Y., Zhang, R., Park, J., Shechtman, E., Paris, S., & Park, T. (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/cvpr52729.2023.00976 · Cited by 492
Pivotal Tuning for Latent-based Editing of Real Images
Roich, D., Mokady, R., Bermano, A. H., & Cohen‐Or, D. (2022) · ACM Transactions on Graphics · doi:10.1145/3544777 · Cited by 481
StyTr 2 : Image Style Transfer with Transformers
Deng, Y., Tang, F., Dong, W., Ma, C., Pan, X., Wang, L., & Xu, C. (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/cvpr52688.2022.01104 · Cited by 460
VBench: Comprehensive Benchmark Suite for Video Generative Models
Huang, Z., He, Y., Yu, J., Zhang, F., Si, C., Jiang, Y., Zhang, Y., Wu, T., Jin, Q., Chanpaisit, N., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02060 · Cited by 407
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Wu, Z., Lischinski, D., & Shechtman, E. (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/cvpr46437.2021.01267 · Cited by 435
Global Texture Enhancement for Fake Face Detection in the Wild
Liu, Z., Qi, X., & Torr, P. H. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00808 · Cited by 401
Expanding functional protein sequence spaces using generative adversarial networks
Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00310-5 · Cited by 388
Large Scale Adversarial Representation LearningPreprint
Donahue, J., & Simonyan, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02544 · Cited by 418
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
Choi, S., Lee, S., Kim, Y., Kim, T., & Kim, C. (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/cvpr42600.2020.01027 · Cited by 404
Differentiable Augmentation for Data-Efficient GAN TrainingPreprint
Zhao, S., Liu, Z., Lin, J., Zhu, J.-Y., & Han, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10738 · Cited by 451
The ethics of algorithms: key problems and solutions
Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2022) · AI & SOCIETY · doi:10.1007/s00146-021-01154-8 · Cited by 384
Learning Self-Consistency for Deepfake Detection
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., & Xia, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01475 · Cited by 383
Reducing Domain Gap by Reducing Style Bias
Nam, H., Lee, H., Park, J., Yoon, W., & Yoo, D. (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/cvpr46437.2021.00858 · Cited by 394
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., et al. (2023) · ACM Computing Surveys · doi:10.1145/3533382 · Cited by 382
Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation
Zhou, H., Sun, Y., Wu, W., Loy, C. C., Wang, X., & Liu, 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/cvpr46437.2021.00416 · Cited by 391
Bengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2024) · IEEE Access · doi:10.1109/access.2024.3397775 · Cited by 470
SRM: A Style-Based Recalibration Module for Convolutional Neural Networks
Lee, H., Kim, H., & Nam, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00194 · Cited by 379
Paint by Example: Exemplar-based Image Editing with Diffusion Models
Yang, B., Gu, S., Zhang, B., Zhang, T., Chen, X., Sun, X., Chen, D., & Wen, F. (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/cvpr52729.2023.01763 · Cited by 459
A Survey on Generative Adversarial Networks: Variants, Applications, and Training
Jabbar, A., Li, X., & Omar, B. (2021) · ACM Computing Surveys · doi:10.1145/3463475 · Cited by 406
On Data Augmentation for GAN Training
Tran, N.-T., Tran, V.-H., Nguyen, N.-B., Nguyen, T.-K., & Cheung, N. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3049346 · Cited by 412
Deep Generative Models in Engineering Design: A Review
Regenwetter, L., Nobari, A. H., & Ahmed, F. (2022) · Journal of Mechanical Design · doi:10.1115/1.4053859 · Cited by 450
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., & Mello, S. D. (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/cvpr52729.2023.00289 · Cited by 461
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
Lu, Y., Chen, D., Olaniyi, E. O., & Huang, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107208 · Cited by 415
DeepFake Detection by Analyzing Convolutional Traces
Guarnera, L., Giudice, O., & Battiato, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00341 · Cited by 374
StyleRig: Rigging StyleGAN for 3D Control Over Portrait Images
Tewari, A., Elgharib, M. A., Bharaj, G., Bernard, F., Seidel, H., Pérez, P., Zollhöfer, M., & Theobalt, C. (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/cvpr42600.2020.00618 · Cited by 362
Yu, H., & Guo, Y. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1183162 · Cited by 357
A U-Net Based Discriminator for Generative Adversarial Networks
Schönfeld, E., Schiele, B., & Khoreva, A. (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/cvpr42600.2020.00823 · Cited by 358
What Makes Fake Images Detectable? Understanding Properties that Generalize
Chai, L., Bau, D., Lim, S.-N., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_7 · Cited by 357
Deep Learning for Medical Image-Based Cancer Diagnosis
Jiang, X., Hu, Z., Wang, S., & Zhang, Y. (2023) · Cancers · doi:10.3390/cancers15143608 · Cited by 409
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
Xia, W., Yang, Y., Xue, J.-H., & Wu, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00229 · Cited by 376
Data Augmentation techniques in time series domain: a survey and taxonomy
Iglesias, G., Talavera, E., González-Prieto, Á., Mozo, A., & Gómez-Canaval, S. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08459-3 · Cited by 347
Avrahami, O., Fried, O., & Lischinski, D. (2023) · ACM Transactions on Graphics · doi:10.1145/3592450 · Cited by 345
Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation
Wang, H., Du, X., Li, J., Yeh, R. A., & Shakhnarovich, G. (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/cvpr52729.2023.01214 · Cited by 446
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00768 · Cited by 340
Pros and cons of GAN evaluation measures: New developments
Borji, A. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103329 · Cited by 381
Single-Source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification
Zhang, Y., Li, W., Sun, W., Tao, R., & Du, Q. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3243853 · Cited by 359
Diffusion Autoencoders: Toward a Meaningful and Decodable Representation
Preechakul, K., Chatthee, N., Wizadwongsa, S., & Suwajanakorn, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01036 · Cited by 338
Seeing What a GAN Cannot GeneratePreprint
(2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 346
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
Deng, Y., Yang, J., Chen, D., Wen, F., & Tong, X. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00520 · Cited by 329
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
Tao, M., Tang, H., Wu, F., Jing, X., Bao, B., & Xu, C. (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/cvpr52688.2022.01602 · Cited by 513
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
Sauer, A., Schwarz, K., & Geiger, A. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530738 · Cited by 353
Li, J., Xie, H., Li, J., Wang, Z., & Zhang, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00639 · Cited by 357
García-Peñalvo, F. J. (2023) · Education in the Knowledge Society (EKS) · doi:10.14201/eks.31279 · Cited by 331
Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires
Sainburg, T., Thielk, M., & Gentner, T. Q. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008228 · Cited by 342
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale UpPreprint
Jiang, Y., Chang, S., & Wang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07074 · Cited by 419
EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction
Cai, H., Li, J., Hu, M., Gan, C., & Han, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01587 · Cited by 356
Image Processing Using Multi-Code GAN Prior
Gu, J., Shen, Y., & Zhou, B. (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/cvpr42600.2020.00308 · Cited by 327
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
Gong, X., Chang, S., Jiang, Y., & Wang, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00332 · Cited by 323
Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning
Engelmann, J., & Lessmann, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114582 · Cited by 358
Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123076 · Cited by 317
Cross-Domain Few-Shot Classification via Learned Feature-Wise TransformationPreprint
Tseng, H.-Y., Lee, H.-Y., Huang, J., & Yang, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.08735 · Cited by 368
GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisPreprint
Schwarz, K., Liao, Y., Niemeyer, M., & Geiger, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02442 · Cited by 400
DIRE for Diffusion-Generated Image Detection
Wang, Z., Bao, J., Zhou, W., Wang, W., Hu, H., Chen, H., & Li, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02051 · Cited by 354
Generative Adversarial Networks for face generation: A survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 370
Deep Gait Recognition: A Survey
Sepas‐Moghaddam, A., & Etemad, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3151865 · Cited by 344
Unsupervised deep learning for super-resolution reconstruction of turbulence
Kim, H., Kim, J., Won, S., & Lee, C. (2021) · Journal of Fluid Mechanics · doi:10.1017/jfm.2020.1028 · Cited by 326
Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content
Yang, H. M., Zhang, R., Guo, X., Liu, W., Zuo, W., & Luo, P. (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/cvpr42600.2020.00787 · Cited by 322
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement
Alaluf, Y., Patashnik, O., & Cohen‐Or, D. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00664 · Cited by 310
High-Fidelity Generative Image CompressionPreprint
Mentzer, F., Toderici, G. D., Tschannen, M., & Agustsson, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09965 · Cited by 347
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
Hukkelås, H., Mester, R., & Lindseth, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33720-9_44 · Cited by 326
High-Fidelity Pluralistic Image Completion with Transformers
Wan, Z., Zhang, J., Chen, D., & Liao, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00465 · Cited by 316
Advances in Adversarial Attacks and Defenses in Computer Vision: A Survey
Akhtar, N., Mian, A. S., Kardan, N., & Shah, M. (2021) · IEEE Access · doi:10.1109/access.2021.3127960 · Cited by 355
GAN Prior Embedded Network for Blind Face Restoration in the Wild
Yang, T., Ren, P., Xie, X., & Zhang, L. (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/cvpr46437.2021.00073 · Cited by 330
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 334
“the desired precision. Another, more appropriate, approach is to exchange these regions by some automatically generated proposals. For example, exchanging all faces for automatically generated ones2 [25]. Such anonymized data will not violate GDPR rules as the original person is no longer identifiable. We aim to ensure…”
BackgroundA comprehensive survey on regularization strategies in machine learning
Tian, Y., & Zhang, Y. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.005 · Cited by 337
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
Chen, D., Yu, N., Zhang, Y., & Fritz, M. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3372297.3417238 · Cited by 311
GANalyze: Toward Visual Definitions of Cognitive Image Properties
Goetschalckx, L., Andonian, A., Oliva, A., & Isola, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00584 · Cited by 294
DeepFake Detection for Human Face Images and Videos: A Survey
Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N. (2022) · IEEE Access · doi:10.1109/access.2022.3151186 · Cited by 297
GANSynth: Adversarial Neural Audio SynthesisPreprint
Engel, J., Agrawal, K. K., Chen, S., Gulrajani, I., Donahue, C., & Roberts, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08710 · Cited by 378
Compositional Visual Generation with Composable Diffusion Models
Liu, N., Li, S., Du, Y., Torralba, A. B., & Tenenbaum, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19790-1_26 · Cited by 365
Implicit Diffusion Models for Continuous Super-Resolution
Gao, S., Liu, X., Zeng, B., Xu, S. H., Li, Y., Luo, X., Liu, J., Zhen, X., & Zhang, B. (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/cvpr52729.2023.00966 · Cited by 325
Improved Precision and Recall Metric for Assessing Generative Models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., & Aila, T. (2019) · arXiv · doi:10.48550/arxiv.1904.06991 · Cited by 379
Self-citationSelf-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J. Y., Liang, Y., Pang, G., Song, D., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3387317 · Cited by 295
“The generator in the GAN can generate synthetic data close to the real data, so adversarial representation learning has a wide range of applications in the data generation field [149], especially in image generation [150], [151], [152], [153].”
BackgroundOn Aliased Resizing and Surprising Subtleties in GAN Evaluation
Parmar, G., Zhang, R., & Zhu, J.-Y. (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/cvpr52688.2022.01112 · Cited by 358
Text2Mesh: Text-Driven Neural Stylization for Meshes
Michel, O., Bar‐On, R., Liu, R., Benaim, S., & Hanocka, R. (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/cvpr52688.2022.01313 · Cited by 359
Autoregressive Image Generation using Residual Quantization
Lee, D., Kim, C., Kim, S., Cho, M., & Han, W.-S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01123 · Cited by 445
GANs for Medical Image Synthesis: An Empirical Study
Skandarani, Y., Jodoin, P., & Lalande, A. (2023) · Journal of Imaging · doi:10.3390/jimaging9030069 · Cited by 354
Advancing High Fidelity Identity Swapping for Forgery Detection
Li, L., Bao, J., Yang, H., Chen, D., & Wen, F. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00512 · Cited by 288
Integrating prediction and reconstruction for anomaly detection
Tang, Y., Zhao, L., Zhang, S., Gong, C., Li, G., & Yang, J. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.024 · Cited by 296
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
Pan, X., Zhan, X., Dai, B., Lin, D., Loy, C. C., & Luo, P. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3115428 · Cited by 312
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
Chan, K. C. K., Wang, X., Xu, X., Gu, J., & Loy, C. C. (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/cvpr46437.2021.01402 · Cited by 285
Semantic photo manipulation with a generative image priorPreprint
Bau, D., Strobelt, H., Peebles, W., Wulff, J., Zhou, B., Zhu, J.-Y., & Torralba, A. B. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 288
A Survey on Deep Learning for Human Activity Recognition
Gu, F., Chung, M.-H., Chignell, M., Valaee, S., Zhou, B., & Liu, X. (2021) · ACM Computing Surveys · doi:10.1145/3472290 · Cited by 301
Editing in Style: Uncovering the Local Semantics of GANs
Collins, E., Bala, R., Price, B., & Susstrunk, S. E. (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/cvpr42600.2020.00581 · Cited by 272
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning
Tian, Z., Cui, L., Liang, J., & Yu, S. (2022) · ACM Computing Surveys · doi:10.1145/3551636 · Cited by 293
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
An, J., Huang, S., Song, Y., Dou, D., Liu, W., & Luo, J. (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/cvpr46437.2021.00092 · Cited by 276
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
Alaluf, Y., Tov, O., Mokady, R., Gal, R., & Bermano, A. H. (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/cvpr52688.2022.01796 · Cited by 291
An introduction to deep generative modeling
Ruthotto, L., & Haber, E. (2021) · GAMM-Mitteilungen · doi:10.1002/gamm.202100008 · Cited by 309
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Wang, Z., She, Q., & Ward, T. E. (2022) · ACM Computing Surveys · doi:10.1145/3439723 · Cited by 267
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
Maximov, M., Elezi, I., & Leal-Taixé, L. (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/cvpr42600.2020.00549 · Cited by 283
Controllable Person Image Synthesis With Attribute-Decomposed GAN
Men, Y., Mao, Y., Jiang, Y., Ma, W., & Lian, Z. (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/cvpr42600.2020.00513 · Cited by 276
Representative Forgery Mining for Fake Face Detection
Wang, C., & Deng, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01468 · Cited by 288
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
Karnewar, A., & Wang, O. (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/cvpr42600.2020.00782 · Cited by 265
CLIPstyler: Image Style Transfer with a Single Text Condition
Kwon, G., & Ye, J. C. (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/cvpr52688.2022.01753 · Cited by 291
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
