MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
This work addresses the challenge of generating realistic synthetic tabular data for applications in data-scarce scenarios, representing an incremental improvement over existing methods.
The paper tackled the problem of synthetic tabular data generation under low sample conditions, where existing models like CTGAN underperform in utility and statistical properties, and proposed MargCTGAN, which improved downstream utility and statistical measures by incorporating feature matching of de-correlated marginals.
The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream task usefulness, often neglecting the importance of statistical properties. This oversight becomes particularly prominent in low sample scenarios, accompanied by a swift deterioration of these statistical measures. In this paper, we address this issue by conducting an evaluation of three state-of-the-art synthetic tabular data generators based on their marginal distribution, column-pair correlation, joint distribution and downstream task utility performance across high to low sample regimes. The popular CTGAN model shows strong utility, but underperforms in low sample settings in terms of utility. To overcome this limitation, we propose MargCTGAN that adds feature matching of de-correlated marginals, which results in a consistent improvement in downstream utility as well as statistical properties of the synthetic data.