Effectively Unbiased FID and Inception Score and Where to Find Them
Open accessAbstract
This paper shows that two commonly used evaluation metrics for generative models, the Fréchet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score. Worse, the paper shows that the bias term depends on the particular model being evaluated, so model A may get a better score than model B simply because model A's bias term is smaller. This effect cannot be fixed by evaluating at a fixed number of samples. This means all comparisons using FID or IS as currently computed are unreliable. We then show how to extrapolate the score to obtain an effectively bias-free estimate of scores computed with an infinite number of samples, which we term FID Infinity and IS Infinity. In turn, this effectively bias-free estimate requires good estimates of scores with a finite number of samples. We show that using Quasi-Monte Carlo integration notably improves estimates of FID and IS for finite sample sets. Our extrapolated scores are simple, drop-in replacements for the finite sample scores. Additionally, we show that using low discrepancy sequence in GAN training offers small improvements in the resulting generator.
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
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?
FID gauges similarity in style and content between two image sets; a lower FID score signifies greater resemblance.
“FID is crucial for gauging the similarity in style and content between two image sets, where a lower FID score signifies greater resemblance.”
From ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12 (2024) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
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 · see citing works
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747 · see citing works
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · see citing works
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017) · arXiv · doi:10.48550/arxiv.1710.10196 · Cited by 4,727 · see citing works
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works
Spectral Normalization for Generative Adversarial Networks
Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018) · arXiv · doi:10.48550/arxiv.1802.05957 · Cited by 3,438 · see citing works
A Note on the Generation of Random Normal Deviates
Box, G. E. P., & Muller, M. E. (1958) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177706645 · Cited by 3,430 · see citing works
On the distribution of points in a cube and the approximate evaluation of integrals
Sobol', I. M. (1967) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(67)90144-9 · Cited by 2,975 · see citing works
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00143 · Cited by 1,923 · see citing works
Options: A Monte Carlo approach
Boyle, P. P. (1977) · Journal of Financial Economics · doi:10.1016/0304-405x(77)90005-8 · Cited by 1,341 · see citing works
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2856256 · Cited by 1,222 · see citing works
Are GANs Created Equal? A Large-Scale StudyPreprint
Lučić, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2017) · Neural Information Processing Systems · doi:10.48550/arxiv.1711.10337 · Cited by 1,175 · see citing works
Stochastic Simulation: Algorithms and Analysis
Asmussen, S., & Glynn, P. W. (2007) · Applications of mathematics · doi:10.1007/978-0-387-69033-9 · Cited by 749 · see citing works
Few-Shot Unsupervised Image-to-Image Translation
Liu, M.-Y., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., & Kautz, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01065 · Cited by 673 · see citing works
Global Illumination using Photon Maps
Jensen, H. W. (1996) · Eurographics · doi:10.1007/978-3-7091-7484-5_3 · Cited by 624 · see citing works
High-dimensional integration: The quasi-Monte Carlo way
Dick, J., Kuo, F. Y., & Sloan, I. H. (2013) · Acta Numerica · doi:10.1017/s0962492913000044 · Cited by 615 · see citing works
Demystifying MMD GANsPreprint
Bińkowski, M., Sutherland, D. J., Arbel, M., & Gretton, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01401 · Cited by 614 · see citing works
Algorithm 247: Radical-inverse quasi-random point sequence
Halton, J. H. (1964) · Communications of the ACM · doi:10.1145/355588.365104 · Cited by 609 · see citing works
Randomly Permuted (t,m,s)-Nets and (t, s)-Sequences
Owen, A. B. (1995) · Lecture notes in statistics · doi:10.1007/978-1-4612-2552-2_19 · Cited by 407 · see citing works
Faster Valuation of Financial Derivatives
Paskov, S. H., & Traub, J. F. (1995) · The Journal of Portfolio Management · doi:10.3905/jpm.1995.409541 · Cited by 369 · see citing works
A Note on the Inception ScorePreprint
Barratt, S. T., & Sharma, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01973 · Cited by 366 · see citing works
Wasserstein Auto-EncodersPreprint
Tolstikhin, I., Bousquet, O., Gelly, S., & Schölkopf, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.01558 · Cited by 329 · see citing works
An empirical study on evaluation metrics of generative adversarial networksPreprint
Xu, Q., Huang, G. M., Yuan, Y., Guo, C., Sun, Y., Wu, F., & Weinberger, K. Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07755 · Cited by 223 · see citing works
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative ModelsPreprint
Zhou, S., Gordon, M., Krishna, R., Narcomey, A., Fei-Fei, L., & Bernstein, M. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01121 · Cited by 105 · see citing works
Few-Shot Unsupervised Image-to-Image TranslationPreprint
Liu, M.-Y., Huang, X. C., Mallya, A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01723 · Cited by 79 · see citing works
Bias Correction of Learned Generative Models using Likelihood-Free Importance WeightingPreprint
Grover, A., Song, J., Agarwal, A., Tran, K., Kapoor, A., Horvitz, E., & Ermon, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09531 · Cited by 73 · see citing works
BoTorch: Programmable Bayesian Optimization in PyTorch.
Balandat, M., Karrer, B., Jiang, D., Daulton, S., Letham, B., Wilson, A. G., & Bakshy, E. (2019) · Cited by 60 · see citing works
Dick, J. (2011) · The Annals of Statistics · doi:10.1214/11-aos880 · Cited by 49 · see citing works
Seeing is Not Necessarily Believing: Limitations of BigGANs for Data Augmentation
Ravuri, S. V., & Vinyals, O. (2019) · Cited by 44 · see citing works
Generating low-discrepancy sequences from the normal distribution: Box–Muller or inverse transform?
Ökten, G., & Göncü, A. (2010) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2010.12.011 · Cited by 43 · see citing works
Randomization of lattice rules for numerical multiple integration
Joe, S. (1990) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(90)90172-v · Cited by 22 · see citing works
Randomization of lattice rules for numerical multiple integration
JoeS. (1990) · Journal of Computational and Applied Mathematics · doi:10.5555/90089.90104 · Cited by 21 · see citing works
HYPE: Human eYe Perceptual Evaluation of Generative ModelsPreprint
Zhou, S., Gordon, M., Krishna, R., Narcomey, A., Morina, D., & Bernstein, M. S. (2019) · arXiv (Cornell University) · Cited by 15 · see citing works
Quasi-Monte Carlo Variational InferencePreprint
Buchholz, A., Wenzel, F., & Mandt, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.01604 · Cited by 13 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014. 4
- Sergei Abramovich Smolyak. Quadrature and interpolation formulas for tensor products of certain classes of functions. In Doklady Akademii Nauk, volume 148, pages 1042-1045. Russian Academy of Sciences, 1963. 8
- Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiao- gang Wang, Xiaolei Huang, and Dimitris N Metaxas. Stack- gan++: Realistic image synthesis with stacked generative ad- versarial networks. IEEE transactions on pattern analysis and machine intelligence, 41(8):1947-1962, 2018. 1
Cited by
Sorted by citations
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 349 · see citing works
On 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 307 · see citing works
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 243 · see citing works
Islam, S., Aziz, M. T., Nabil, H. R., Jim, J. R., Mridha, M. F., Kabir, M. M., Asai, N., & Shin, J. (2024) · IEEE Access · doi:10.1109/access.2024.3370848 · Cited by 185 · see citing works
Lumiere: A Space-Time Diffusion Model for Video Generation
Bar-Tal, O., Chefer, H., Tov, O., Herrmann, C., Paiss, R., Zada, S., Ephrat, A., Hur, J., Liu, G., Raj, A., et al. (2024) · SIGGRAPH Asia Conference Papers · doi:10.1145/3680528.3687614 · Cited by 161 · see citing works
A Survey of Synthetic Data Augmentation Methods in Machine Vision
Mumuni, A., Mumuni, F., & Gerrar, N. K. (2024) · Machine Intelligence Research · doi:10.1007/s11633-022-1411-7 · Cited by 148 · see citing works
Thermal Infrared Image Colorization for Nighttime Driving Scenes With Top-Down Guided Attention
Luo, F., Li, Y., Zeng, G., Peng, P., Wang, G., & Li, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3145476 · Cited by 115 · see citing works
ArtFID: Quantitative Evaluation of Neural Style Transfer
Wright, M., & Ommer, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16788-1_34 · Cited by 99 · see citing works
Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation
Schütte, A. D., Hetzel, J., Gatidis, S., Hepp, T., Dietz, B., Bauer, S., & Schwab, P. (2021) · Europe PMC (PubMed Central) · doi:10.3929/ethz-b-000507963 · Cited by 90 · see citing works
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
Osuala, R. O., Kushibar, K., Garrucho, L., Linardos, A., Szafranowska, Z., Klein, S., Glocker, B., Díaz, O., & Lekadir, K. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102704 · Cited by 96 · see citing works
Generative AI in mobile networks: a survey
Karapantelakis, A., Alizadeh, P., Alabbasi, A., Dey, K., & Nikou, A. (2023) · Annals of Telecommunications · doi:10.1007/s12243-023-00980-9 · Cited by 85 · see citing works
Nazir, A., Hussain, A. B., Singh, M., & Assad, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19694-8 · Cited by 82 · see citing works
Meta-Learning Based Domain Prior With Application to Optical-ISAR Image Translation
Liao, H., Xia, J., Yang, Z., Pan, F., Liu, Z., & Liu, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3318401 · Cited by 71 · see citing works
RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model
Bie, F., Yang, Y., Zhou, Z., Ghanem, A., Zhang, M., Yao, Z., Wu, X., Holmes, C., Golnari, P. A., Clifton, D. A., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3522305 · Cited by 76 · see citing works
Generative adversarial networks for prognostic and health management of industrial systems: A review
Li, Q. '., Tang, Y., & Chu, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124341 · Cited by 64 · see citing works
Deep Learning in Virtual Try-On: A Comprehensive Survey
Islam, T., Miron, A., Liu, X., & Li, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3368612 · Cited by 68 · see citing works
JoJoGAN: One Shot Face Stylization
Chong, M. J., & Forsyth, D. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19787-1_8 · Cited by 59 · see citing works
Self-citationNetDiffus: Network traffic generation by diffusion models through time-series imaging
Sivaroopan, N., Bandara, D., Madarasingha, C., Jourjon, G., Jayasumana, A. P., & Thilakarathna, K. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110616 · Cited by 65 · see citing works
Chen, L., Xiao, S., Chen, Y., Song, Y., Wu, R., & Sun, L. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642229 · Cited by 60 · see citing works
Evaluating generative models in high energy physics
Kansal, R., Li, A., Duarte, J. M., Chernyavskaya, N., Maurizio Pierini, B., Orzari, B., & Tomei, T. R. F. P. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.076017 · Cited by 55 · see citing works
FlexIT: Towards Flexible Semantic Image Translation
Couairon, G., Grechka, A., Verbeek, J. J., Schwenk, H., & Cord, 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/cvpr52688.2022.01773 · Cited by 47 · see citing works
Intelligent generation of Peking opera facial masks with deep learning frameworks
Yan, M. G., Xiong, R., Shen, Y., Jin, C., & Wang, Y. (2023) · Heritage Science · doi:10.1186/s40494-023-00865-z · Cited by 47 · see citing works
Research on Ship Anomaly Detection Algorithm Based on Transformer-GSA Encoder
Liu, H., Wu, C., Li, B., Zong, Z., & Shu, Y. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3536483 · Cited by 45 · see citing works
Adapting Frechet Audio Distance for Generative Music Evaluation
Gui, A., Gamper, H., Braun, S., & Emmanouilidou, D. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446663 · Cited by 44 · see citing works
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization
Maharana, A., & Bansal, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.543 · Cited by 40 · see citing works
Diverse Hyperspectral Remote Sensing Image Synthesis With Diffusion Models
Liu, L., Chen, B., Chen, H., Zou, Z., & Shi, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3335975 · Cited by 40 · see citing works
Sonar Image Generation by MFA-CycleGAN for Boosting Underwater Object Detection of AUVs
Zhou, J., Li, Y., Qin, H., Dai, P., Zhao, Z., & Hu, M. (2024) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2024.3350746 · Cited by 39 · see citing works
Toward Accurate and Realistic Outfits Visualization with Attention to Details
Li, K., Chong, M. J., Zhang, J., & Liu, 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/cvpr46437.2021.01529 · Cited by 39 · see citing works
Self-citationDALLE-URBAN: Capturing the urban design expertise of large text to image transformers
Seneviratne, S., Senanayake, D., Rasnayaka, S., Vidanaarachchi, R., & Thompson, J. (2022) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034603 · Cited by 42 · see citing works
WeedGan: a novel generative adversarial network for cotton weed identification
Sharma, V., Tripathi, A. K., Mittal, H., Parmar, A., Soni, A., & Amarwal, R. (2022) · The Visual Computer · doi:10.1007/s00371-022-02742-5 · Cited by 37 · see citing works
On Buggy Resizing Libraries and Surprising Subtleties in FID CalculationPreprint
Parmar, G., Zhang, R., & Zhu, J.-Y. (2021) · arXiv (Cornell University) · Cited by 35 · see citing works
Wang, Y., Zhang, Q., Wang, G., & Cheng, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10818-y · Cited by 33 · see citing works
Generative Adversarial Network Applications in Industry 4.0: A Review
Akar, C. A., Massih, R. A., Yaghi, A., Khalil, J., Kamradt, M., & Makhoul, A. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-023-01966-9 · Cited by 31 · see citing works
Deep generative models for detector signature simulation: A taxonomic review
Hashemi, H., & Krause, C. (2024) · Reviews in Physics · doi:10.1016/j.revip.2024.100092 · Cited by 35 · see citing works
TTVAE: Transformer-based generative modeling for tabular data generation
Wang, A. X., & Nguyen, B. P. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2025.104292 · Cited by 29 · see citing works
All-optical synthesis chip for large-scale intelligent semantic vision generation
Chen, Y., Sun, X., Tan, L., Jiang, Y., Zhou, Y., Zhang, W., & Zhai, G. (2025) · Science · doi:10.1126/science.adv7434 · Cited by 31 · see citing works
Evaluation Metrics for Generative Models: An Empirical Study
Betzalel, E., Penso, C., & Fetaya, E. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6030073 · Cited by 26 · see citing works
Guided Co-Modulated GAN for 360° Field of View Extrapolation
Dastjerdi, M. R. K., Hold-Geoffroy, Y., Eisenmann, J., Khodadadeh, S., & Lalonde, J.-F. (2022) · International Conference on 3D Vision · doi:10.1109/3dv57658.2022.00059 · Cited by 43 · see citing works
Synthetic Medical Imaging Generation with Generative Adversarial Networks for Plain Radiographs
McNulty, J., Kho, L., Case, A. L., Slater, D. M., Abzug, J. M., & Russell, S. A. (2024) · Applied Sciences · doi:10.3390/app14156831 · Cited by 24 · see citing works
Deep Generative Models for 3D Medical Image Synthesis
Friedrich, P., Frisch, Y., & Cattin, P. C. (2024) · Springer eBooks · doi:10.1007/978-3-031-80965-1_13 · Cited by 23 · see citing works
Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval
Chong, M. J., Chu, W., Kumar, A., & Forsyth, D. A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00386 · Cited by 23 · see citing works
Self-citationSSIS-Seg: Simulation-Supervised Image Synthesis for Surgical Instrument Segmentation
Colleoni, E., Psychogyios, D., van Amsterdam, B., Vasconcelos, F., & Stoyanov, D. V. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3178549 · Cited by 23 · see citing works
VATr++: Choose Your Words Wisely for Handwritten Text Generation
Vanherle, B., Pippi, V., Cascianelli, S., Michiels, N., Reeth, F. V., & Cucchiara, R. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3481154 · Cited by 22 · see citing works
Multi-Sentence Complementarily Generation for Text-to-Image Synthesis
Zhao, L., Huang, P., Chen, T., Fu, C., Hu, Q., & Zhang, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3297769 · Cited by 22 · see citing works
Classifier-guided multi-style tile image generation method
Lu, J., Shi, M., Song, C., Zhao, W., Xi, L., & Emam, M. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101899 · Cited by 21 · see citing works
Faria, V., de Queiroz, A. R., & DeCarolis, J. F. (2023) · Energy · doi:10.1016/j.energy.2023.126946 · Cited by 20 · see citing works
POVNet: Image-Based Virtual Try-On Through Accurate Warping and Residual
Li, K., Zhang, J., & Forsyth, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3283302 · Cited by 20 · see citing works
Self-citationDeterministic Autoencoder using Wasserstein loss for tabular data generation
Wang, A. X., & Nguyen, B. P. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107208 · Cited by 19 · see citing works
Unconditional latent diffusion models memorize patient imaging data
Dar, S. U. H., Seyfarth, M., Ayx, I., Papavassiliu, T., Schoenberg, S. O., Siepmann, R. M., Laqua, F. C., Kahmann, J., Frey, N., Baeßler, B., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01468-8 · Cited by 22 · see citing works
Detail Me More: Improving GAN’s photo-realism of complex scenes
Gadde, R., Feng, Q., & Martı́nez, A. M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01369 · Cited by 18 · see citing works
Adversarial Learning-Based Data Augmentation for Palm-Vein Identification
Qin, H., Xi, H., Li, Y., El‐Yacoubi, M. A., Wang, J., & Gao, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3334825 · Cited by 18 · see citing works
StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGANPreprint
Chong, M. J., Lee, H.-Y., & Forsyth, D. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.01619 · Cited by 17 · see citing works
Self-citationArmandpour, M., Sadeghian, A., Li, C., & Zhou, M. (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/cvpr46437.2021.00506 · Cited by 15 · see citing works
Smart GAN: a smart generative adversarial network for limited imbalanced dataset
Kumari, D., Vyshnavi, S., Dhar, R., Rajita, B. S. A. S., Panda, S., & Christopher, J. J. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06198-3 · Cited by 15 · see citing works
Al-Hindawi, F., Siddiquee, M. M. R., Wu, T., Hu, H., & Sun, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107255 · Cited by 13 · see citing works
Guided image generation for improved surgical image segmentation
Colleoni, E., Sánchez-Matilla, R., Luengo, I., & Stoyanov, D. V. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103263 · Cited by 13 · see citing works
A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.
Osuala, R. O., Kushibar, K., Garrucho, L., Linardos, A., Szafranowska, Z., Klein, S., Glocker, B., Díaz, O., & Lekadir, K. (2021) · Spiral (Imperial College London) · Cited by 13 · see citing works
Speech driven facial animation generation based on GAN
Li, X., Zhang, J., & Liu, Y. (2022) · Displays · doi:10.1016/j.displa.2022.102260 · Cited by 12 · see citing works
Evaluating generative networks using Gaussian mixtures of image features
Luzi, L., Marrero, C. O., Wynar, N., Baraniuk, R. G., & Henry, M. J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00036 · Cited by 11 · see citing works
Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer
Drees, L., Weber, I., Rußwurm, M., & Roscher, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16788-1_30 · Cited by 11 · see citing works
Chen, Y., Yan, Y., Wang, X., & Zheng, Y. (2023) · Sensors · doi:10.3390/s23146531 · Cited by 11 · see citing works
Ge, R., Fang, Z., Wei, P., Chen, Z., Jiang, H., Elazab, A., Li, W., Wan, X., Zhang, S., & Wang, C. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3394597 · Cited by 11 · see citing works
Hou, J., Lu, Y., Wang, M., Ouyang, W., Yang, Y., Zou, F., Gu, B., & Liu, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112233 · Cited by 10 · see citing works
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Chatterjee, A., Stan, G. B. M., Aflalo, E., Paul, S., Ghosh, D., Gokhale, T., Schmidt, L., Hajishirzi, H., Lal, V., Baral, C. R., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72670-5_12 · Cited by 12 · see citing works
Zhou, X., Hu, Y., Jia, Q., & Xie, R. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3480932 · Cited by 10 · see citing works
Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation
Schütte, A. D., Hetzel, J., Gatidis, S., Hepp, T., Dietz, B., Bauer, S., & Schwab, P. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00507-3 · Cited by 93 · see citing works
Few-shot image generation with reverse contrastive learning
Gou, Y., Li, M., Zhang, Y., He, Z., & He, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.026 · Cited by 10 · see citing works
Additional look into GAN-based augmentation for deep learning COVID-19 image classification
Fedoruk, O., Klimaszewski, K., Ogonowski, A., & Kruk, M. (2023) · Machine Graphics and Vision · doi:10.22630/mgv.2023.32.3.6 · Cited by 10 · see citing works
What's in a Decade? Transforming Faces Through Time
Chen, E. M., Sun, J. H., Khandelwal, A., Lischinski, D., Snavely, N., & Averbuch‐Elor, H. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14761 · Cited by 9 · see citing works
Image Synthesis Using GANs and Diffusion Models
Karn, A. K., Kumar, S., Kushwaha, S. K., & Katarya, R. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10263208 · Cited by 9 · see citing works
Cultural Relevance Index: Measuring Cultural Relevance in AI-Generated Images
Elsharif, W., Agus, M., Alzubaidi, M., & She, J. P. M. (2024) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr62202.2024.00071 · Cited by 9 · see citing works
Fang, Z., Yu, X. F., Zhou, G., Zhuang, K., Chen, Y., Ge, R., Wang, C., Jia, G., Wu, Q., Ye, J., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126471 · Cited by 9 · see citing works
A novel flexible identity-net with diffusion models for painting-style generation
Zhao, Y., Liang, Z., Qiu, Y., Wang, X., & Yang, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-12434-4 · Cited by 12 · see citing works
Text to Image GANs with RoBERTa and Fine-grained Attention Networks
Siddharth, M., & Aarthi, R. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.01212115 · Cited by 9 · see citing works
Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes
Kulshreshtha, P., Lianos, N., Pugh, B., & Jiddi, S. (2022) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct57072.2022.00181 · Cited by 9 · see citing works
Evaluating and Explaining Generative Adversarial Networks for Continual Learning under Concept Drift
Guzy, F., Woźniak, M., & Krawczyk, B. (2021) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw53433.2021.00044 · Cited by 9 · see citing works
Phienphanich, P., Lerthirunvibul, N., Charnnarong, E., Munthuli, A., Tantibundhit, C., & Suwanwela, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3287389 · Cited by 9 · see citing works
Multilingual Conceptual Coverage in Text-to-Image Models
Saxon, M., & Wang, W. Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.266 · Cited by 10 · see citing works
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use CasePreprint
Chadebec, C., Vincent, L. J., & Allassonnière, S. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.08309 · Cited by 9 · see citing works
Side-Scan Sonar Image Generator Based on Diffusion Models for Autonomous Underwater Vehicles
Zhang, F., Hou, X., Wang, Z., Cheng, C., & Tan, T. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12081457 · Cited by 8 · see citing works
Adversarial Identity Injection for Semantic Face Image Synthesis
Tarollo, G., Fontanini, T., Ferrari, C., Borghi, G., & Prati, A. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00154 · Cited by 8 · see citing works
ForgetMe: Benchmarking the selective forgetting capabilities of generative models
Yu, Z., Idris, M. Y. I., Wang, P., Xia, Y., & Xiang, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112087 · Cited by 13 · see citing works
Controlled Multi-modal Image Generation for Plant Growth Modeling
Miranda, M., Drees, L., & Roscher, R. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956115 · Cited by 8 · see citing works
Translation-Enhanced Multilingual Text-to-Image Generation
Li, Y., Chang, C.-Y., Rawls, S., Vulić, I., & Korhonen, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.510 · Cited by 8 · see citing works
High-Precision Surface Crack Detection for Rolling Steel Production Equipment in ICPS
Peng, Y., Wang, C., Hao, Y., Zhen, L., Chen, G., Kumar, N., & Yu, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3302317 · Cited by 8 · see citing works
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance
Hu, Z., Hu, K., Mo, C., Pan, L., & Wang, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i11.29150 · Cited by 8 · see citing works
TrapNet: Model Inversion Defense via Trapdoor
Ma, W.-L., Wang, D.-R., Song, Y., Xue, M.-H., Wen, S., Li, Z.-D., & Xiang, Y. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3560557 · Cited by 11 · see citing works
Zhao, H., Li, W., Huang, D., Huang, J., & Zhang, L. (2024) · The Visual Computer · doi:10.1007/s00371-024-03585-y · Cited by 7 · see citing works
Selecting which Dense Retriever to use for Zero-Shot Search
Khramtsova, E., Zhuang, S., Baktashmotlagh, M., Wang, X., & Zuccon, G. (2023) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3624918.3625330 · Cited by 8 · see citing works
Use of Auxiliary Classifier Generative Adversarial Network in Touchstroke Authentication
Deb, D., & Guirguis, M. M. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00049 · Cited by 7 · see citing works
Style Transfer of Thangka Images Highlighting Style Attributes
Hu, W., Song, H., Zhang, F., Zhao, Y., & Shi, X. (2023) · IEEE Access · doi:10.1109/access.2023.3318258 · Cited by 6 · see citing works
Elevating Wafer Defect Inspection with Denoising Diffusion Probabilistic Model
Wu, P.-H., Hoang, T. P., Chou, Y.-T., Mayol, A. P., Lai, Y.-W., Kang, C.-H., Chan, Y.-C., Lin, S.-Z., & Chen, S. (2024) · Mathematics · doi:10.3390/math12203164 · Cited by 6 · see citing works
Ji, Y., Liu, X., & Zheng, H. R. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103598 · Cited by 6 · see citing works
Rethinking cross-domain semantic relation for few-shot image generation
Gou, Y., Li, M., Lv, Y., Zhang, Y., Xing, Y., & He, Y. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04602-8 · Cited by 6 · see citing works
Ku, D., & Pahk, H.-J. (2024) · IEEE Access · doi:10.1109/access.2024.3380618 · Cited by 6 · see citing works
Audio–visual self-supervised representation learning: A survey
Alsuwat, M. S., Al-Shareef, S., & Alghamdi, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129750 · Cited by 7 · see citing works
On the Robustness of Quality Measures for GANs
Alfarra, M., Pérez, J. C., Frühstück, A., Torr, P. H. S., Wonka, P., & Ghanem, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19790-1_2 · Cited by 6 · see citing works
Naghizadeh, A., Xu, H., Mohamed, M., Metaxas, D., & Liu, D. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00392 · Cited by 6 · see citing works
Toward Accurate and Realistic Virtual Try-on Through Shape Matching and Multiple WarpsPreprint
Li, K., Chong, M. J., Liu, J., & Forsyth, D. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.10817 · Cited by 6 · see citing works
Self-citationWu, Z., Wei, C., Xia, Y., & Ji, Z. (2024) · Applied Sciences · doi:10.3390/app142311391 · Cited by 5 · see citing works
- Authors
- Chong, M. J., & Forsyth, D.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6069-6078
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2985516053
- Cited by
- 242
- References
- 43
- Citations by year
- 2026: 26 · 2025: 59 · 2024: 69 · 2023: 43 · 2022: 24 · 2021: 18 · 2020: 3
- FWCI
- 6.78
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- Fréchet Inception Distance, Inception Score, generative adversarial networks, quasi-Monte Carlo integration, low-discrepancy sequences, GAN training, generative model evaluation
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
