arXiv:2502.17119v2 Announce Type: replace 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 VAEs have achieved useful results, but they can suffer from unstable training, mode collapse, weak modeli
The rapid progress of deep generative models in other data modalities is now being systematically applied and surveyed for tabular data, indicating a maturation of techniques like diffusion and flow matching in this complex domain.
Improved generative models for tabular data can enable more sophisticated synthetic data generation, privacy-preserving data sharing, and advanced simulation for various industries previously limited by data constraints.
The ability to reliably generate high-quality tabular data will transform data-driven decision-making, allowing for more robust AI training, scenario planning, and compliance in sectors handling sensitive information.
- · Data scientists
- · Healthcare industry
- · Financial services
- · AI model developers
- · Legacy data anonymization techniques
- · Companies relying on scarce real-world data for model training
More accurate and diverse synthetic tabular datasets become readily available for research and development.
Accelerated AI development in domains like finance and healthcare due to readily accessible, albeit synthetic, data.
New privacy-preserving data sharing paradigms emerge, potentially disrupting traditional data market dynamics and regulatory frameworks.
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Read at arXiv cs.LG