mlsanityData to 14 Oct 2026

Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study

arXiv 2112.01925 ↗First posted 3 Dec 2021cs.LGcited by 5

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Abstract (arXiv)

Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data. So far much of this work has been applied to use cases outside of the data confidentiality domain with a common application being the production of artificial images. Here we consider the potential application of GANs for the purpose of generating synthetic census microdata. We employ a battery of utility metrics and a disclosure risk metric (the Targeted Correct Attribution Probability) to compare the data produced by tabular GANs with those produced using orthodox data synthesis methods.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 3 Dec 2021.

Builds on

17 of 56 references.

References · 17

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  5. 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
  6. 2019Modeling Tabular data using Conditional GANcited by 699
  7. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  8. 2017Are GANs Created Equal? A Large-Scale Studycited by 267
  9. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  10. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  11. 2018Data Synthesis based on Generative Adversarial Networkscited by 185
  12. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
5 more
  1. 2018Synthesizing Tabular Data using Generative Adversarial Networkscited by 92
  2. 2018Patch-Based Image Inpainting with Generative Adversarial Networkscited by 70
  3. 2018Image Colorization with Generative Adversarial Networkscited by 24
  4. 2018Generating Multi-Categorical Samples with Generative Adversarial Networkscited by 17
  5. 2018Generative Adversarial Network for Medical Images (MI-GAN)cited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2025Flow Matching for Tabular Data Synthesiscited by 6
  2. 2022Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdatacited by 4
  3. 2025Enhancing the Scalability of Classical Surrogates for Real-World Quantum Machine Learning Applicationscited by 1
  4. 2023Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation methodcited by 0
  5. 2026Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesiscited by 0