Generative AI for Synthetic Data Generation in Machine Learning: A Review and Evaluation Framework

References

[1] Chen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F. Synthetic data in machine learning for medicine and healthcare. Nature Biomedical Engineering. 2021;5:493–497. https://doi.org/10.1038/s41551-021-00751-8

[2] Goodfellow I, et al. Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS); 2014. p. 2672–2680.

[3] Kingma DP, Welling M. Auto-encoding variational Bayes. In: Proceedings of the International Conference on Learning Representations (ICLR); 2014. arXiv:1312.6114.

[4] Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (NeurIPS); 2020. p. 6840–6851.

[5] Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K. Modeling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems (NeurIPS); 2019.

[6] Kotelnikov A, Baranchuk D, Rubachev I, Babenko A. TabDDPM: Modelling tabular data with diffusion models. In: Proc. 40th Int. Conf. Machine Learning (ICML); 2023. p. 17564–17579.

[7] Borisov V, Seßler K, Leemann T, Pawelczyk M, Kasneci G. Language models are realistic tabular data generators. In: Proc. Int. Conf. Learning Representations (ICLR); 2023.

[8] Jordon J, Szpruch L, Houssiau F, Bottarelli M, Cherubin G, Maple C, et al. Synthetic data—what, why and how? arXiv:2205.03257 [Preprint]. 2022.

[9] Esteban C, Hyland SL, Rätsch G. Real-valued (medical) time series generation with recurrent conditional GANs. arXiv:1706.02633 [Preprint]. 2017.

[10] Jordon J, Yoon J, van der Schaar M. PATE-GAN: Generating synthetic data with differential privacy guarantees. In: Proc. Int. Conf. Learning Representations (ICLR); 2019.

[11] Dwork C, McSherry F, Nissim K, Smith A. Calibrating noise to sensitivity in private data analysis. In: Proc. Theory of Cryptography Conf. (TCC); 2006. p. 265–284.

[12] Shokri R, Stronati M, Song C, Shmatikov V. Membership inference attacks against machine learning models. In: Proc. IEEE Symp. Security and Privacy (S&P); 2017. p. 3–18.

[13] Shumailov I, Shumaylov Z, Zhao Y, Papernot N, Anderson R, Gal Y. AI models collapse when trained on recursively generated data. Nature. 2024;631:755–759. https://doi.org/10.1038/s41586-024-07566-y

[14] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research. 2002;16:321–357.

@article{1724321,
      author = {Shravani Mahadev Karvande},
      title = {Generative AI for Synthetic Data Generation in Machine Learning: A Review and Evaluation Framework},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {3882-3887},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724321.pdf},
      abstract = {Machine learning models need large, representative, labeled datasets, but real data are often scarce, imbalanced, costly to label, or restricted by privacy rules. Generative AI can produce synthetic data that keeps the statistical structure of a source dataset without reusing its records, although memorization remains a risk. This paper reviews four generator families: generative adversarial networks, variational autoencoders, diffusion models, and language-model-based tabular generators. It organizes evaluation along three axes: fidelity to real data, downstream utility for model training, and privacy leakage. A worked example shows how synthetic minority samples can rebalance a skewed dataset. The paper then proposes an evaluation protocol that combines train-on-synthetic, test-on-real experiments, differential-privacy accounting, and membership-inference testing. No measured performance result is claimed. The contribution is a structured comparison and an evaluation plan for researchers and practitioners.},
      keywords = {synthetic data generation; generative adversarial networks; variational autoencoders; diffusion models; tabular data; data augmentation; privacy-preserving machine learning},
      month = {October},
  }