mlsanityData to 7 Oct 2026

CTAB-GAN: Effective Table Data Synthesizing

arXiv 2102.08369 ↗First posted 16 Feb 2021cs.LGcited by 156

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from generative Adversarial Networks (GAN) and address two main data types in the industry, i.e., continuous and categorical. In this paper, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types, including a mix of continuous and categorical variables. Moreover, we address data imbalance and long-tail issues, i.e., certain variables have drastic frequency differences across large values. To achieve those aims, we first introduce the information loss and classification loss to the conditional GAN. Secondly, we design a novel conditional vector, which efficiently encodes the mixed data type and skewed distribution of data variable. We extensively evaluate CTAB-GAN with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results into higher accuracy for five machine learning algorithms, by up to 17%.

Key facts. Cited by 156 papers in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

16 of 30 references.

References · 16

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  3. 2017Improved Training of Wasserstein GANscited by 2,223
  4. 2019Modeling Tabular data using Conditional GANcited by 695
  5. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  6. 2017Deep & Cross Network for Ad Click Predictionscited by 392
  7. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  8. 2018Data Synthesis based on Generative Adversarial Networkscited by 184
  9. 2015On Wasserstein Two Sample Testing and Related Families of Nonparametric Testscited by 165
  10. 2017The Cramer Distance as a Solution to Biased Wasserstein Gradientscited by 116
  11. 2017PacGAN: The power of two samples in generative adversarial networkscited by 104
  12. 2018Synthesizing Tabular Data using Generative Adversarial Networkscited by 92
4 more
  1. 2020Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learningcited by 67
  2. 2018Airline Passenger Name Record Generation using Generative Adversarial Networkscited by 19
  3. 2017Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectoriescited by 8
  4. 2020QActor: On-line Active Learning for Noisy Labeled Stream Datacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 15; 2023: 26; 2024: 46; 2025: 39; 2026: 24.

156 papers.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  2. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  3. 2023REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformerscited by 93
  4. 2023CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesiscited by 78
  5. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  6. 2022Synthetic Data -- what, why and how?cited by 66
  7. 2023Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Treescited by 60
  8. 2023TabuLa: Harnessing Language Models for Tabular Data Synthesiscited by 42
  9. 2022Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluationcited by 39
  10. 2023TabMT: Generating tabular data with masked transformerscited by 36
  11. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  12. 2024ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Modelscited by 28
38 more
  1. 2022TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Datacited by 27
  2. 2024SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Datacited by 27
  3. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 27
  4. 2023AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizingcited by 24
  5. 2024Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analyticscited by 18
  6. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Modelscited by 17
  7. 2024Systematic Assessment of Tabular Data Synthesiscited by 17
  8. 2023DiSK: A Diffusion Model for Structured Knowledgecited by 16
  9. 2023Continuous Diffusion for Mixed-Type Tabular Datacited by 16
  10. 2024HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protectioncited by 16
  11. 2024Diffusion Models for Tabular Data Imputation and Synthetic Data Generationcited by 15
  12. 2024TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Modelscited by 15
  13. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  14. 2024EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Modelscited by 14
  15. 2023DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generationcited by 13
  16. 2024An improved tabular data generator with VAE-GMM integrationcited by 13
  17. 2024CTSyn: A Foundation Model for Cross Tabular Data Generationcited by 13
  18. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 13
  19. 2025A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyondcited by 13
  20. 2025The DCR Delusion: Measuring the Privacy Risk of Synthetic Datacited by 11
  21. 2024SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Modelscited by 10
  22. 2025Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesiscited by 10
  23. 2021Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Datacited by 9
  24. 2023Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generationcited by 9
  25. 2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
  26. 2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8
  27. 2024Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Classcited by 8
  28. 2025A Large Language Model for Feasible and Diverse Population Synthesiscited by 8
  29. 2025Joint Relational Database Generation via Graph-Conditional Diffusion Modelscited by 8
  30. 2021DTGAN: Differential Private Training for Tabular GANscited by 7
  31. 2024FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generationcited by 7
  32. 2024Structured Evaluation of Synthetic Tabular Datacited by 7
  33. 2025Synthetic Data Privacy Metricscited by 7
  34. 2025Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Modelscited by 7
  35. 2022Permutation-Invariant Tabular Data Synthesiscited by 6
  36. 2024FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Modelscited by 6
  37. 2024Synthetic Tabular Data Validation: A Divergence-Based Approachcited by 6
  38. 2024Watermarking Generative Tabular Datacited by 6

50 most cited of 156 All 156 (JSON)