Diffusion and Flow Matching Models for Tabular Data: A Survey

📰 ArXiv cs.AI

Learn how diffusion and flow matching models generate high-quality tabular data, overcoming challenges like missing values and complex dependencies

advanced Published 25 May 2026

Action Steps

Read the survey to understand diffusion and flow matching models for tabular data
Apply diffusion models to generate synthetic tabular data with numerical and categorical attributes
Use flow matching models to handle missing values and imbalanced categories in tabular data
Evaluate the performance of diffusion and flow matching models on your dataset
Compare the results with traditional GAN-based methods for tabular data generation

Who Needs to Know This

Data scientists and machine learning engineers working with tabular data can benefit from this survey to improve their generative modeling skills

Key Insight

💡 Diffusion and flow matching models can effectively generate tabular data with complex dependencies and missing values

Key Takeaways

Learn how diffusion and flow matching models generate high-quality tabular data, overcoming challenges like missing values and complex dependencies

Full Article

Title: Diffusion and Flow Matching Models for Tabular Data: A Survey

Abstract:
arXiv:2502.17119v2 Announce Type: replace-cross Abstract: Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, generative modeling remains difficult: a dataset may contain numerical and categorical attributes, missing values, sensitive fields, imbalanced categories, complex feature dependencies, and domain constraints. Earlier tabular data modeling methods based on GANs or

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