  Advancements in science rely on data sharing. In medicine, where personal
data are often involved, synthetic tabular data generated by generative
adversarial networks (GANs) offer a promising avenue. However, existing GANs
struggle to capture the complexities of real-world tabular data, which often
contain a mix of continuous and categorical variables with potential imbalances
and dependencies. We propose a novel correlation- and mean-aware loss function
designed to address these challenges as a regularizer for GANs. To ensure a
rigorous evaluation, we establish a comprehensive benchmarking framework using
ten real-world datasets and eight established tabular GAN baselines. The
proposed loss function demonstrates statistically significant improvements over
existing methods in capturing the true data distribution, significantly
enhancing the quality of synthetic data generated with GANs. The benchmarking
framework shows that the enhanced synthetic data quality leads to improved
performance in downstream machine learning (ML) tasks, ultimately paving the
way for easier data sharing.
