mlsanityData to 14 Oct 2026

On the Usefulness of Synthetic Tabular Data Generation

arXiv 2306.15636 ↗First posted 27 Jun 2023cs.LGcited by 12

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Abstract (arXiv)

Despite recent advances in synthetic data generation, the scientific community still lacks a unified consensus on its usefulness. It is commonly believed that synthetic data can be used for both data exchange and boosting machine learning (ML) training. Privacy-preserving synthetic data generation can accelerate data exchange for downstream tasks, but there is not enough evidence to show how or why synthetic data can boost ML training. In this study, we benchmarked ML performance using synthetic tabular data for four use cases: data sharing, data augmentation, class balancing, and data summarization. We observed marginal improvements for the balancing use case on some datasets. However, we conclude that there is not enough evidence to claim that synthetic tabular data is useful for ML training.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 19 of its 25 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

19 of 25 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
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  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2013Auto-Encoding Variational Bayescited by 4,053
  5. 2014Conditional Generative Adversarial Netscited by 2,162
  6. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  7. 2019Modeling Tabular data using Conditional GANcited by 699
  8. 2019Neural Spline Flowscited by 596
  9. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
  10. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 377
  11. 2022Language Models are Realistic Tabular Data Generatorscited by 294
  12. 2016Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learningcited by 195
7 more
  1. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  2. 2022STaSy: Score-based Tabular data Synthesiscited by 86
  3. 2023Synthcity: facilitating innovative use cases of synthetic data in different data modalitiescited by 76
  4. 2022Private Synthetic Data for Multitask Learning and Marginal Queriescited by 35
  5. 2022SOS: Score-based Oversampling for Tabular Datacited by 29
  6. 2022To SMOTE, or not to SMOTE?cited by 14
  7. 2023Generative Oversampling for Imbalanced Data via Majority-Guided VAEcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 4.

12 papers.

  1. 2024TabPFGen -- Tabular Data Generation with TabPFNcited by 28
  2. 2024TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Modelscited by 16
  3. 2024CTSyn: A Foundation Model for Cross Tabular Data Generationcited by 14
  4. 2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8
  5. 2025Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Modelscited by 8
  6. 2024TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsettingcited by 5
  7. 2025Synthetic Data Generation for Augmenting Small Samplescited by 3
  8. 2025Assessing Generative Models for Structured Datacited by 1
  9. 2026Active Tabular Augmentation via Policy-Guided Diffusion Inpaintingcited by 1
  10. 2026Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentationcited by 0
  11. 2026Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Frameworkcited by 0
  12. 2026Task-Conditioned Synthetic Data Generation for Improving Machine Learning Performance in Agricultural Prediction Taskscited by 0