MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
Tejumade Afonja Dingfan Chen Mario Fritz
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.