  This paper introduces a bi-discriminator GAN for synthesizing tabular
datasets containing continuous, binary, and discrete columns. Our proposed
approach employs an adapted preprocessing scheme and a novel conditional term
for the generator network to more effectively capture the input sample
distributions. Additionally, we implement straightforward yet effective
architectures for discriminator networks aiming at providing more
discriminative gradient information to the generator. Our experimental results
on four benchmarking public datasets corroborates the superior performance of
our GAN both in terms of likelihood fitness metric and machine learning
efficacy.
