mlsanityData to 14 Oct 2026

CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis

arXiv 2304.12654 ↗First posted 25 Apr 2023cs.LG cs.AIcited by 78

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis models become sophisticated and realistic. However, there still exists a difficulty in modeling discrete variables (columns) of tabular data. In this work, we propose to process continuous and discrete variables separately (but being conditioned on each other) by two diffusion models. The two diffusion models are co-evolved during training by reading conditions from each other. In order to further bind the diffusion models, moreover, we introduce a contrastive learning method with a negative sampling method. In our experiments with 11 real-world tabular datasets and 8 baseline methods, we prove the efficacy of the proposed method, called CoDi.

Key facts. Cited by 78 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 25 of its 57 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

25 of 57 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022STaSy: Score-based Tabular data Synthesis8 Oct

Other references · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  6. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  7. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  8. 2019Modeling Tabular data using Conditional GANcited by 699
  9. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 574
  10. 2021Tabular Data: Deep Learning is Not All You Needcited by 535
  11. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
  12. 2021Deep Neural Networks and Tabular Data: A Surveycited by 491
12 more
  1. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  2. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 398
  3. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  4. 2020How to train your neural ODE: the world of Jacobian and kinetic regularizationcited by 228
  5. 2017VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learningcited by 200
  6. 2018Data Synthesis based on Generative Adversarial Networkscited by 185
  7. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  8. 2022SOS: Score-based Oversampling for Tabular Datacited by 29
  9. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
  10. 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
  11. 2018Generating Synthetic but Plausible Healthcare Record Datasetscited by 12
  12. 2022An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Modelscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 22; 2025: 27; 2026: 20.

78 papers.

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  1. 2023Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Spacecited by 186
  2. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  3. 2024TabDiff: a Mixed-type Diffusion Model for Tabular Data Generationcited by 63
  4. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
  5. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
  6. 2024ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Modelscited by 28
  7. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 28
  8. 2024Language-driven Grasp Detectioncited by 26
  9. 2023AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizingcited by 24
  10. 2025Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spacescited by 21
  11. 2024A Survey on Diffusion Models for Recommender Systemscited by 20
  12. 2024Systematic Assessment of Tabular Data Synthesiscited by 18
38 more
  1. 2023Continuous Diffusion for Mixed-Type Tabular Datacited by 17
  2. 2023DiSK: A Diffusion Model for Structured Knowledgecited by 16
  3. 2024Self-Supervision Improves Diffusion Models for Tabular Data Imputationcited by 16
  4. 2024TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Modelscited by 16
  5. 2024Diffusion Models for Tabular Data Imputation and Synthetic Data Generationcited by 15
  6. 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AIcited by 15
  7. 2025A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyondcited by 15
  8. 2025Exponential Family Variational Flow Matching for Tabular Data Generationcited by 15
  9. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 14
  10. 2023CuTS: Customizable Tabular Synthetic Data Generationcited by 13
  11. 2025A Survey on Diffusion Models for Anomaly Detectioncited by 13
  12. 2024Balanced Mixed-Type Tabular Data Synthesis with Diffusion Modelscited by 12
  13. 2025TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generationcited by 10
  14. 2024DiffLM: Controllable Synthetic Data Generation via Diffusion Language Modelscited by 9
  15. 2025LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusioncited by 9
  16. 2023A Comprehensive Survey on Generative Diffusion Models for Structured Datacited by 8
  17. 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 8
  18. 2025Diffusion and Flow Matching Models for Tabular Data: A Surveycited by 8
  19. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  20. 2023Boosting Data Analytics With Synthetic Volume Expansioncited by 7
  21. 2024Understanding and Mitigating Memorization in Diffusion Models for Tabular Datacited by 7
  22. 2024AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Modelscited by 6
  23. 2024Tabular Data Synthesis with Differential Privacy: A Surveycited by 6
  24. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 6
  25. 2024Guided Discrete Diffusion for Electronic Health Record Generationcited by 5
  26. 2024TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Datacited by 5
  27. 2024Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Trainingcited by 5
  28. 2025TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representationcited by 5
  29. 2025Diffusion Transformers for Tabular Data Time Series Generationcited by 5
  30. 2025SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modelingcited by 5
  31. 2024Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Datacited by 4
  32. 2026Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Featurescited by 4
  33. 2025Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generationcited by 3
  34. 2025A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatmentscited by 3
  35. 2025CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generationcited by 3
  36. 2025A Generalized Theory of Mixup for Structure-Preserving Synthetic Datacited by 2
  37. 2026MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular datacited by 2
  38. 2026Learning to Interrupt in Language-based Multi-agent Communicationcited by 2

50 most cited of 78 All 78 (JSON)

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  1. 2024CTSyn: A Foundation Model for Cross Tabular Data Generation
  2. 2026TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks
  3. 2026CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data
  4. 2026Generating Benchmark Health Data Using a Tabular Diffusion Transformer
  5. 2020Tabular GANs for uneven distribution
  6. 2023MissDiff: Training Diffusion Models on Tabular Data with Missing Values
  7. 2022TabDDPM: Modelling Tabular Data with Diffusion Models
  8. 2026MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation