{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:56:04Z","timestamp":1776102964682,"version":"3.50.1"},"reference-count":99,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mathematics"],"abstract":"<jats:p>Synthetic data consists of artificially generated data. When data are scarce, or of poor quality, synthetic data can be used, for example, to improve the performance of machine learning models. Generative adversarial networks (GANs) are a state-of-the-art deep generative models that can generate novel synthetic samples that follow the underlying data distribution of the original dataset. Reviews on synthetic data generation and on GANs have already been written. However, none in the relevant literature, to the best of our knowledge, has explicitly combined these two topics. This survey aims to fill this gap and provide useful material to new researchers in this field. That is, we aim to provide a survey that combines synthetic data generation and GANs, and that can act as a good and strong starting point for new researchers in the field, so that they have a general overview of the key contributions and useful references. We have conducted a review of the state-of-the-art by querying four major databases: Web of Sciences (WoS), Scopus, IEEE Xplore, and ACM Digital Library. This allowed us to gain insights into the most relevant authors, the most relevant scientific journals in the area, the most cited papers, the most significant research areas, the most important institutions, and the most relevant GAN architectures. GANs were thoroughly reviewed, as well as their most common training problems, their most important breakthroughs, and a focus on GAN architectures for tabular data. Further, the main algorithms for generating synthetic data, their applications and our thoughts on these methods are also expressed. Finally, we reviewed the main techniques for evaluating the quality of synthetic data (especially tabular data) and provided a schematic overview of the information presented in this paper.<\/jats:p>","DOI":"10.3390\/math10152733","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:15:26Z","timestamp":1659485726000},"page":"2733","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":350,"title":["Survey on Synthetic Data Generation, Evaluation Methods and GANs"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0507-7504","authenticated-orcid":false,"given":"Alvaro","family":"Figueira","sequence":"first","affiliation":[{"name":"CRACS-INESC TEC, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-2739","authenticated-orcid":false,"given":"Bruno","family":"Vaz","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, University of Porto, Rua do Campo Alegre, s\/n, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"ref_1","unstructured":"Emam, K., Mosquera, L., and Hoptroff, R. (2020). Chapter 1: Introducing Synthetic Data Generation. Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data, O\u2019Reilly Media, Inc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W.Y., and Mao, B.H. (2005, January 23\u201326). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. Proceedings of the International Conference on Intelligent Computing, Hefei, China.","DOI":"10.1007\/11538059_91"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat, C., Sinapiromsaran, K., and Lursinsap, C. (2009, January 27\u201330). Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkok, Thailand.","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"ref_5","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2018.06.056","article-title":"Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE","volume":"465","author":"Douzas","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_7","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_8","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, QC, Canada."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s00162-021-00593-9","article-title":"Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks","volume":"35","author":"Siddani","year":"2021","journal-title":"Theor. Comput. Fluid Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Soares, C., and Torgo, L. (2021). GANs for Tabular Healthcare Data Generation: A Review on Utility and Privacy. Discovery Science, Springer International Publishing.","DOI":"10.1007\/978-3-030-88942-5"},{"key":"ref_11","unstructured":"Gupta, A., Vedaldi, A., and Zisserman, A. (July, January 26). Synthetic data for text localisation in natural images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10115-012-0487-8","article-title":"A review of feature selection methods on synthetic data","volume":"34","year":"2013","journal-title":"Knowl. Inf. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., and Greenspan, H. (2018, January 4\u20137). Synthetic data augmentation using GAN for improved liver lesion classification. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363576"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.isprsjprs.2010.09.001","article-title":"Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment","volume":"65","author":"Koch","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"IM35","DOI":"10.1190\/geo2018-0646.1","article-title":"FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation","volume":"84","author":"Wu","year":"2019","journal-title":"Geophysics"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nikolenko, S.I. (2021). Synthetic Data Outside Computer Vision. Synthetic Data for Deep Learning, Springer.","DOI":"10.1007\/978-3-030-75178-4"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","article-title":"Recent progress on generative adversarial networks (GANs): A survey","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","unstructured":"Di Mattia, F., Galeone, P., De Simoni, M., and Ghelfi, E. (2019). A survey on gans for anomaly detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446374","article-title":"Generative adversarial networks (GANs) challenges, solutions, and future directions","volume":"54","author":"Saxena","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Q., Gao, J., Lin, W., and Yuan, Y. (2019, January 16\u201317). Learning from synthetic data for crowd counting in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00839"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Atapour-Abarghouei, A., and Breckon, T.P. (2018, January 18\u201323). Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00296"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1109\/TII.2018.2885365","article-title":"A small-sample wind turbine fault detection method with synthetic fault data using generative adversarial nets","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/TIP.2018.2879249","article-title":"Synthetic data generation for end-to-end thermal infrared tracking","volume":"28","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s11263-020-01365-4","article-title":"Pixel-wise crowd understanding via synthetic data","volume":"129","author":"Wang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Chen, X., and Gool, L.V. (2019, January 15\u201320). Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00194"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18295","DOI":"10.1038\/s41598-019-54244-5","article-title":"DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data","volume":"9","author":"Dunn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"54207","DOI":"10.1109\/ACCESS.2018.2872025","article-title":"Autoencoder-combined generative adversarial networks for synthetic image data generation and detection of jellyfish swarm","volume":"6","author":"Kim","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Torkzadehmahani, R., Kairouz, P., and Paten, B. (2019, January 16\u201317). Dp-cgan: Differentially private synthetic data and label generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00018"},{"key":"ref_29","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_30","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_31","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016, January 5\u201310). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_32","first-page":"469","article-title":"Coupled generative adversarial networks","volume":"29","author":"Liu","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","unstructured":"Odena, A., Olah, C., and Shlens, J. (2017, January 6\u201311). Conditional image synthesis with auxiliary classifier gans. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D.N. (2017, January 22\u201329). Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref_35","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dong, H.W., Hsiao, W.Y., Yang, L.C., and Yang, Y.H. (2017). MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment. arXiv.","DOI":"10.1609\/aaai.v32i1.11312"},{"key":"ref_38","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv."},{"key":"ref_39","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_40","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016). Improved Techniques for Training GANs. arXiv."},{"key":"ref_41","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2018). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. arXiv."},{"key":"ref_42","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., and Zhu, J.Y. (2019, January 15\u201320). Semantic image synthesis with spatially-adaptive normalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"48451","DOI":"10.1109\/ACCESS.2020.2979348","article-title":"PiiGAN: Generative adversarial networks for pluralistic image inpainting","volume":"8","author":"Cai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","unstructured":"Prangemeier, T., Reich, C., Wildner, C., and Koeppl, H. (2021). Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy. arXiv."},{"key":"ref_47","unstructured":"Xu, L., and Veeramachaneni, K. (2018). Synthesizing tabular data using generative adversarial networks. arXiv."},{"key":"ref_48","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TIT.1968.1054142","article-title":"Approximating discrete probability distributions with dependence trees","volume":"14","author":"Chow","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3134428","article-title":"Privbayes: Private data release via bayesian networks","volume":"42","author":"Zhang","year":"2017","journal-title":"ACM Trans. Database Syst. (TODS)"},{"key":"ref_51","unstructured":"Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W.F., and Sun, J. (2017, January 18\u201319). Generating multi-label discrete patient records using generative adversarial networks. Proceedings of the Machine Learning for Healthcare Conference, PMLR, Boston, MA, USA."},{"key":"ref_52","unstructured":"Srivastava, A., Valkov, L., Russell, C., Gutmann, M.U., and Sutton, C. (2017, January 4\u20139). Veegan: Reducing mode collapse in gans using implicit variational learning. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., and Kim, Y. (2018). Data synthesis based on generative adversarial networks. arXiv.","DOI":"10.14778\/3231751.3231757"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Rajabi, A., and Garibay, O.O. (2021). TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks. arXiv.","DOI":"10.3390\/make4020022"},{"key":"ref_55","unstructured":"Andrews, G. (2022, February 14). What Is Synthetic Data?. Available online: https:\/\/blogs.nvidia.com\/blog\/2021\/06\/08\/what-is-synthetic-data\/."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Alanazi, Y., Sato, N., Ambrozewicz, P., Blin, A.N.H., Melnitchouk, W., Battaglieri, M., Liu, T., and Li, Y. (2021). A survey of machine learning-based physics event generation. arXiv.","DOI":"10.24963\/ijcai.2021\/588"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Assefa, S. (2020, January 15\u201316). Generating synthetic data in finance: Opportunities, challenges and pitfalls. Proceedings of the International Conference on AI in Finance, New York, NY, USA.","DOI":"10.1145\/3383455.3422554"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"164","DOI":"10.3389\/fpubh.2020.00164","article-title":"Generative Adversarial Networks and Its Applications in Biomedical Informatics","volume":"8","author":"Lan","year":"2020","journal-title":"Front. Public Health"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chen, J., and Little, J.J. (2019, January 16\u201320). Sports camera calibration via synthetic data. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00305"},{"key":"ref_60","unstructured":"Barth, R., IJsselmuiden, J., Hemming, J., and van Henten, E.J. (2017, January 28). Optimising realism of synthetic agricultural images using cycle generative adversarial networks. Proceedings of the IEEE IROS Workshop on Agricultural Robotics, Vancouver, BC, Canada."},{"key":"ref_61","unstructured":"Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., and Birchfield, S. (2018). Deep object pose estimation for semantic robotic grasping of household objects. arXiv."},{"key":"ref_62","unstructured":"Nikolenko, S.I. (2019). Synthetic data for deep learning. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Batuwita, R., and Palade, V. (2010, January 18\u201323). Efficient resampling methods for training support vector machines with imbalanced datasets. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain.","DOI":"10.1109\/IJCNN.2010.5596787"},{"key":"ref_64","first-page":"1","article-title":"C4. 5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling","volume":"11","author":"Drummond","year":"2003","journal-title":"Workshop Learn. Imbalanced Datasets II"},{"key":"ref_65","unstructured":"Lusa, L. (2012, January 12\u201315). Evaluation of smote for high-dimensional class-imbalanced microarray data. Proceedings of the 2012 11th International Conference on Machine Learning and Applications, Boca Raton, FL, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ins.2017.10.017","article-title":"Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates","volume":"425","author":"Sun","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.inffus.2019.07.006","article-title":"Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting","volume":"54","author":"Sun","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lee, T., Kim, M., and Kim, S.P. (2020, January 26\u201328). Data augmentation effects using borderline-SMOTE on classification of a P300-based BCI. Proceedings of the 2020 8th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea.","DOI":"10.1109\/BCI48061.2020.9061656"},{"key":"ref_69","first-page":"29","article-title":"Using Gabriel graphs in Borderline-SMOTE to deal with severe two-class imbalance problems on neural networks","volume":"Volume 248","author":"Riafio","year":"2012","journal-title":"Artificial Intelligence Research and Development, Proceedings of the 15th International Conference of the Catalan Association for Artificial Intelligence, Alicante, Spain, 24\u201326 October 2012"},{"key":"ref_70","first-page":"234","article-title":"The effective redistribution for imbalance dataset: Relocating safe-level SMOTE with minority outcast handling","volume":"43","author":"Siriseriwan","year":"2016","journal-title":"Chiang Mai J. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Lu, C., Lin, S., Liu, X., and Shi, H. (2020, January 21\u201324). Telecom fraud identification based on ADASYN and random forest. Proceedings of the 2020 5th International Conference on Computer and Communication Systems (ICCCS), Guangzhou, China.","DOI":"10.1109\/ICCCS49078.2020.9118521"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Aditsania, A., and Saonard, A.L. (2017, January 25\u201326). Handling imbalanced data in churn prediction using ADASYN and backpropagation algorithm. Proceedings of the 2017 3rd International Conference on Science in Information Technology (ICSITech), Bandung, Indonesia.","DOI":"10.1109\/ICSITech.2017.8257170"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Chen, S. (2017, January 25\u201326). Research on Extreme Financial Risk Early Warning Based on ODR-ADASYN-SVM. Proceedings of the 2017 International Conference on Humanities Science, Management and Education Technology (HSMET 2017), Taiyuan, China.","DOI":"10.2991\/hsmet-17.2017.209"},{"key":"ref_74","unstructured":"MacQueen, J. Classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"104616","DOI":"10.1016\/j.ssci.2020.104616","article-title":"Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data","volume":"125","author":"Sarkar","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1145\/1007730.1007737","article-title":"Class imbalances versus small disjuncts","volume":"6","author":"Jo","year":"2004","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_77","unstructured":"Learn, S. (2022, February 23). Gaussian Mixture Models. Available online: https:\/\/scikit-learn.org\/stable\/modules\/mixture.html."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Chokwitthaya, C., Zhu, Y., Mukhopadhyay, S., and Jafari, A. (2020). Applying the Gaussian Mixture Model to Generate Large Synthetic Data from a Small Data Set. Construction Research Congress 2020: Computer Applications, American Society of Civil Engineers.","DOI":"10.1061\/9780784482865.132"},{"key":"ref_79","unstructured":"(2022, February 11). A Comprehensive Introduction to Bayesian Deep Learning. Available online: https:\/\/jorisbaan.nl\/2021\/03\/02\/introduction-to-bayesian-deep-learning."},{"key":"ref_80","unstructured":"Soni, D. (2022, January 29). Introduction to Bayesian Networks. Available online: https:\/\/towardsdatascience.com\/introduction-to-bayesian-networks-81031eeed94e."},{"key":"ref_81","unstructured":"Russell, S.J., Norvig, P., and Chang, M.W. (2022). Chapter 13: Probabilistic Reasoning. Artificial Intelligence: A Modern Approach, Pearson."},{"key":"ref_82","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Chapter 20: Deep Generative Models. Depp Learning, MIT Press."},{"key":"ref_83","unstructured":"Foster, D. (2019). Chapter 3: Variational Autoencoders. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, O\u2019Reilly."},{"key":"ref_84","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Chapter 14: Autoencoders. Depp Learning, MIT Press."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fu, Y., Zang, A., Sigal, L., and Agam, G. (2015). Learning classifiers from synthetic data using a multichannel autoencoder. arXiv.","DOI":"10.1109\/ICMLA.2015.199"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Wan, Z., Zhang, Y., and He, H. (December, January 27). Variational autoencoder based synthetic data generation for imbalanced learning. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA.","DOI":"10.1109\/SSCI.2017.8285168"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"105950","DOI":"10.1016\/j.aap.2020.105950","article-title":"Crash data augmentation using variational autoencoder","volume":"151","author":"Islam","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Fahimi, F., Zhang, Z., Goh, W.B., Ang, K.K., and Guan, C. (2019, January 19\u201322). Towards EEG generation using GANs for BCI applications. Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA.","DOI":"10.1109\/BHI.2019.8834503"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Patel, M., Wang, X., and Mao, S. (2020, January 13). Data augmentation with Conditional GAN for automatic modulation classification. Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, Linz, Austria.","DOI":"10.1145\/3395352.3402622"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2019.06.043","article-title":"MFC-GAN: Class-imbalanced dataset classification using multiple fake class generative adversarial network","volume":"361","author":"Elyan","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Ali-Gombe, A., Elyan, E., Savoye, Y., and Jayne, C. (2018, January 8\u201313). Few-shot classifier GAN. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489387"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Sushko, V., Gall, J., and Khoreva, A. (2021, January 20\u201325). One-shot gan: Learning to generate samples from single images and videos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00293"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"220505","DOI":"10.1103\/PhysRevLett.128.220505","article-title":"Entangling quantum generative adversarial networks","volume":"128","author":"Niu","year":"2022","journal-title":"Phys. Rev. Lett."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ma, Y., Zhu, D., Dong, L., and Liu, Y. (2022). MetroGAN: Simulating Urban Morphology with Generative Adversarial Network. arXiv.","DOI":"10.1145\/3534678.3539239"},{"key":"ref_95","unstructured":"(2022, February 02). Yann LeCun Quora Session Overview. Available online: https:\/\/www.kdnuggets.com\/2016\/08\/yann-lecun-quora-session.html."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/00031305.1973.10478966","article-title":"Graphs in statistical analysis","volume":"27","author":"Anscombe","year":"1973","journal-title":"Am. Stat."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11192-020-03351-6","article-title":"Forecasting emerging technologies using data augmentation and deep learning","volume":"123","author":"Zhou","year":"2020","journal-title":"Scientometrics"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Shmelkov, K., Schmid, C., and Alahari, K. (2018, January 8\u201314). How good is my GAN?. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8_14"},{"key":"ref_99","unstructured":"Alaa, A.M., van Breugel, B., Saveliev, E., and van der Schaar, M. (2021). How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. arXiv."}],"container-title":["Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-7390\/10\/15\/2733\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:01:23Z","timestamp":1760140883000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-7390\/10\/15\/2733"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":99,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["math10152733"],"URL":"https:\/\/doi.org\/10.3390\/math10152733","relation":{},"ISSN":["2227-7390"],"issn-type":[{"value":"2227-7390","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,2]]}}}