Data to 7 Oct 2026

Synthesizing Tabular Data using Generative Adversarial Networks

arXiv 1811.11264 ↗First posted 27 Nov 2018cs.LG stat.MLcited by 92

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Abstract (arXiv)

Generative adversarial networks (GANs) implicitly learn the probability distribution of a dataset and can draw samples from the distribution. This paper presents, Tabular GAN (TGAN), a generative adversarial network which can generate tabular data like medical or educational records. Using the power of deep neural networks, TGAN generates high-quality and fully synthetic tables while simultaneously generating discrete and continuous variables. When we evaluate our model on three datasets, we find that TGAN outperforms conventional statistical generative models in both capturing the correlation between columns and scaling up for large datasets.

Key facts. Cited by 92 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Nov 2018.

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Citing papers per year: 2020: 1; 2021: 9; 2022: 18; 2023: 16; 2024: 20; 2025: 15; 2026: 13.

92 papers.

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2021Deep Neural Networks and Tabular Data: A Surveycited by 489
2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
2022Synthetic Data -- what, why and how?cited by 66
2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
2021Property Inference Attacks Against GANscited by 32
2022TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Datacited by 27
2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 27
2023Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testingcited by 23
2023SurvivalGAN: Generating Time-to-Event Data for Survival Analysiscited by 17
2022Row Conditional-TGAN for generating synthetic relational databasescited by 15
2025A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyondcited by 13
2021Synthesising Multi-Modal Minority Samples for Tabular Datacited by 10

38 more

2021Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Datacited by 9
2022Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Surveycited by 9
2024Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)cited by 9
2022On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulationcited by 8
2022Generating Realistic Synthetic Relational Data through Graph Variational Autoencoderscited by 8
2023FFPDG: Fast, Fair and Private Data Generationcited by 8
2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8
2024Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Modelscited by 7
2021Explainable Machine Learning using Real, Synthetic and Augmented Fire Tests to Predict Fire Resistance and Spalling of RC Columnscited by 6
2021Generative Adversarial Networks for Synthetic Data Generation: A Comparative Studycited by 5
2023Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesiscited by 5
2024Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspectivecited by 5
2024Powering In-Database Dynamic Model Slicing for Structured Data Analyticscited by 5
2022GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutionscited by 4
2023Boosting Synthetic Data Generation with Effective Nonlinear Causal Discoverycited by 4
2023MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regimecited by 4
2023Change Management using Generative Modeling on Digital Twinscited by 4
2023Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directionscited by 4
2024Targeted synthetic data generation for tabular data via hardness characterizationcited by 4
2025Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detectioncited by 4
2022Improving Correlation Capture in Generating Imbalanced Data using Differentially Private Conditional GANscited by 3
2023CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesiscited by 3
2023Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learningcited by 3
2025GReaTER: Generate Realistic Tabular data after data Enhancement and Reductioncited by 3
2025Graph-Conditional Flow Matching for Relational Data Generationcited by 3
2025MedEqualizer: A Framework Investigating Bias in Synthetic Medical Data and Mitigation via Augmentationcited by 3
2023Embeddings for Tabular Data: A Surveycited by 2
2023From Principle to Practice: Vertical Data Minimization for Machine Learningcited by 2
2024Inference Attacks: A Taxonomy, Survey, and Promising Directionscited by 2
2024TabularFM: An Open Framework For Tabular Foundational Modelscited by 2
2024zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generationcited by 2
2025Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasetscited by 2
2025Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarkingcited by 2
2026Improving TabPFN's Synthetic Data Generation by Integrating Causal Structurecited by 2
2022FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurancecited by 1
2022Redactor: A Data-centric and Individualized Defense Against Inference Attackscited by 1
2022Customs Import Declaration Datasetscited by 1
2022OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Datacited by 1

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