mlsanityData to 7 Oct 2026

Machine Learning for Synthetic Data Generation: A Review

arXiv 2302.04062 ↗First posted 8 Feb 2023cs.LGcited by 118

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Abstract (arXiv)

Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations. In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate. This paper presents a comprehensive systematic review of existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains. Additionally, it explores different machine learning methods, with particular emphasis on neural network architectures and deep generative models. The paper also addresses the crucial aspects of privacy and fairness concerns related to synthetic data generation. Furthermore, this study identifies the challenges and opportunities prevalent in this emerging field, shedding light on the potential avenues for future research. By delving into the intricacies of synthetic data generation, this paper aims to contribute to the advancement of knowledge and inspire further exploration in synthetic data generation.

Key facts. Cited by 118 papers in the map (data through 7 Oct 2026). Builds on 114 of its 274 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

114 of 274 references.

References · 114

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  9. 2013Auto-Encoding Variational Bayescited by 3,980
  10. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  11. 2022Video Diffusion Modelscited by 2,280
  12. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
38 more
  1. 2020Extracting Training Data from Large Language Modelscited by 2,144
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  4. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  5. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  6. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  7. 2019Deep Leakage from Gradientscited by 1,004
  8. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  9. 2021Ethical and social risks of harm from Language Modelscited by 957
  10. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  11. 2023Extracting Training Data from Diffusion Modelscited by 723
  12. 2019Graph Neural Networks for Social Recommendationcited by 711
  13. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  14. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  15. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
  16. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
  17. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  18. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  19. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 418
  20. 2018MolGAN: An implicit generative model for small molecular graphscited by 392
  21. 2021Differentially Private Fine-tuning of Language Modelscited by 392
  22. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  23. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  24. 2018Understanding disentangling in β-VAEcited by 332
  25. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  26. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  27. 2019Differential Privacy Has Disparate Impact on Model Accuracycited by 316
  28. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 309
  29. 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 264
  30. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
  31. 2018Differentially Private Generative Adversarial Networkcited by 235
  32. 2018Adversarial Audio Synthesiscited by 222
  33. 2021Fake It Till You Make It: Face analysis in the wild using synthetic data alonecited by 200
  34. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  35. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  36. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  37. 2019Learning from Synthetic Data for Crowd Counting in the Wildcited by 148
  38. 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136

50 most cited of 114 All 114 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 11; 2024: 42; 2025: 45; 2026: 20.

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  1. 2024Best Practices and Lessons Learned on Synthetic Datacited by 104
  2. 2025Unlocking Multimodal Mathematical Reasoning via Process Reward Modelcited by 64
  3. 2023SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examplescited by 53
  4. 2025MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoningcited by 45
  5. 2024The Future of Large Language Model Pre-training is Federatedcited by 30
  6. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  7. 2023Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Setscited by 27
  8. 2024TWIN-GPT: Digital Twins for Clinical Trials via Large Language Modelcited by 24
  9. 2023Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmarkcited by 23
  10. 2024Uncertainty Quantification on Clinical Trial Outcome Predictioncited by 19
  11. 2023TarGEN: Targeted Data Generation with Large Language Modelscited by 17
  12. 2023AI-Generated Images as Data Source: The Dawn of Synthetic Eracited by 13
38 more
  1. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 12
  2. 2024Montessori-Instruct: Generate Influential Training Data Tailored for Student Learningcited by 12
  3. 2025Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directionscited by 11
  4. 2024Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Modelscited by 10
  5. 2025Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classificationcited by 10
  6. 2025Data Augmentation Through Random Style Replacementcited by 9
  7. 2024Time-Causal VAE: Robust Financial Time Series Generatorcited by 8
  8. 2023Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researcherscited by 7
  9. 2023ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Datacited by 7
  10. 2024Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Developmentcited by 7
  11. 2024A Survey on Data Quality Dimensions and Tools for Machine Learningcited by 7
  12. 2025Scale Efficient Training for Large Datasetscited by 7
  13. 2024FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Modelscited by 6
  14. 2024Tabular Data Synthesis with Differential Privacy: A Surveycited by 6
  15. 2025CATCH: A Modular Cross-domain Adaptive Template with Hookcited by 6
  16. 2024Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Interventioncited by 5
  17. 2025New Money: A Systematic Review of Synthetic Data Generation for Financecited by 5
  18. 2024Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Modelscited by 4
  19. 2024Targeted synthetic data generation for tabular data via hardness characterizationcited by 4
  20. 2025Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithmscited by 4
  21. 2025Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Datacited by 4
  22. 2025Bias-Corrected Data Synthesis for Imbalanced Learningcited by 4
  23. 2024Improving Text Generation on Images with Synthetic Captionscited by 3
  24. 2025Stochastic dynamics learning with state-space systemscited by 3
  25. 2023Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generationcited by 2
  26. 2023GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualizationcited by 2
  27. 2024Fusing Pretrained ViTs with TCNet for Enhanced EEG Regressioncited by 2
  28. 2024A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approachescited by 2
  29. 2024Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Modelscited by 2
  30. 2024AI-Native Multi-Access Future Networks -- The REASON Architecturecited by 2
  31. 2024Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entitiescited by 2
  32. 2024High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASRcited by 2
  33. 2025Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognitioncited by 2
  34. 2025Artificial Conversations, Real Results: Fostering Language Detection with Synthetic Datacited by 2
  35. 2025HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questionscited by 2
  36. 2025Synthetic CVs To Build and Test Fairness-Aware Hiring Toolscited by 2
  37. 2025Beyond Real Data: Synthetic Data through the Lens of Regularizationcited by 2
  38. 2025Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judgescited by 2

50 most cited of 118 All 118 (JSON)