A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond

Understand

Generative modelling has become the standard approach for synthesising tabular data.

• However, different use cases demand synthetic data to comply with different requirements to be useful in practice.
• In this survey, we review deep generative modelling approaches for tabular data from the perspective of five types of requirements: utility of the synthetic data, alignment of the synthetic data with domain-specific knowledge, statistical fidelity of the synthetic data distribution compared to the real data distribution, privacy-preserving capabilities, and sampling diversity.
• We group the approaches along two levels of granularity: (i) based on the requirements they address and (ii) according to the underlying model they utilise.