Self-supervised Autoencoder for Correlation-Preserving in Tabular GANs

Preserving relationships and interactions between columns(or variables) is crucial for any synthetic tabular data generation approach. Despite their performances, the existing generative adversarial network (GAN)-based methods don't shed much importance on this aspect. In this work, we propose VSA+GAN, a framework that augments with the existing GANs to capture and learn inter-variable interactions with a self-supervised autoencoder trained on a novel pretext task. We show that the method is versatile and is applicable to any variation of Tabular Generative Adversarial Network implementations, and empirically show that our framework significantly improves their performance in terms of data similarity, pair-wise correlation and machine-learning utility metrics.