mlsanityData to 7 Oct 2026

Modeling Tabular data using Conditional GAN

arXiv 1907.00503 ↗First posted 1 Jul 2019cs.LG stat.MLcited by 695

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Cited by 68 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous columns. Continuous columns may have multiple modes whereas discrete columns are sometimes imbalanced making the modeling difficult. Existing statistical and deep neural network models fail to properly model this type of data. We design TGAN, which uses a conditional generative adversarial network to address these challenges. To aid in a fair and thorough comparison, we design a benchmark with 7 simulated and 8 real datasets and several Bayesian network baselines. TGAN outperforms Bayesian methods on most of the real datasets whereas other deep learning methods could not.

Key facts. Cited by 695 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2019.

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Citing papers per year: 2020: 1; 2021: 33; 2022: 79; 2023: 106; 2024: 142; 2025: 169; 2026: 165.

695 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. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  4. 2023Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Spacecited by 183
  5. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  6. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  7. 2023Synthetic Experience Replaycited by 100
  8. 2023REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformerscited by 93
  9. 2022STaSy: Score-based Tabular data Synthesiscited by 85
  10. 2023CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesiscited by 78
  11. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  12. 2022Synthetic Data -- what, why and how?cited by 66
38 more
  1. 2023Membership Inference Attacks against Synthetic Data through Overfitting Detectioncited by 66
  2. 2023Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Treescited by 60
  3. 2024TabDiff: a Mixed-type Diffusion Model for Tabular Data Generationcited by 60
  4. 2022TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Datacited by 56
  5. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  6. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  7. 2023STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tablescited by 48
  8. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  9. 2023Data-centric AI: Perspectives and Challengescited by 43
  10. 2023TabuLa: Harnessing Language Models for Tabular Data Synthesiscited by 42
  11. 2023FinDiff: Diffusion Models for Financial Tabular Data Generationcited by 41
  12. 2022Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluationcited by 39
  13. 2023MissDiff: Training Diffusion Models on Tabular Data with Missing Valuescited by 38
  14. 2024A Comprehensive Survey on Data Augmentationcited by 38
  15. 2023SoK: Privacy-Preserving Data Synthesiscited by 37
  16. 2023TabMT: Generating tabular data with masked transformerscited by 36
  17. 2021Copula Flows for Synthetic Data Generationcited by 35
  18. 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
  19. 2022Learning Fair Representation via Distributional Contrastive Disentanglementcited by 31
  20. 2023Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Datacited by 31
  21. 2022PTab: Using the Pre-trained Language Model for Modeling Tabular Datacited by 30
  22. 2022SOS: Score-based Oversampling for Tabular Datacited by 29
  23. 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
  24. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  25. 2021Conditional Generative Models for Counterfactual Explanationscited by 28
  26. 2023Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishingcited by 28
  27. 2024ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Modelscited by 28
  28. 2021BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarkscited by 27
  29. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
  30. 2022TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Datacited by 27
  31. 2024SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Datacited by 27
  32. 2024A Systematic Review on Long-Tailed Learningcited by 27
  33. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 27
  34. 2023Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Predictioncited by 26
  35. 2024TabPFGen -- Tabular Data Generation with TabPFNcited by 26
  36. 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
  37. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 24
  38. 2023MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinementcited by 24

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