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MargCTGAN: A "Marginally" Better CTGAN for the Low Sample Regime

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posted on 2024-02-19, 09:36 authored by 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.

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Primary Research Area

  • Trustworthy Information Processing

Journal

CoRR

Volume

abs/2307.07997

Sub Type

  • Article

BibTeX

@article{Afonja:Chen:Fritz:2023, title = {MargCTGAN: A "Marginally" Better CTGAN for the Low Sample Regime}, author = "Afonja, Tejumade" AND "Chen, Dingfan" AND "Fritz, Mario", year = 2023, month = 7, journal = "CoRR" }

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