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 using the ${\chi }_{\beta }^2$ distribution 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.

本文介绍了一种双判别器 GAN，用于合成包含连续、二进制和离散列的表格数据集。我们提出的方法采用了一种经过调整的预处理方案和一个新的条件项，使用${\chi }_{\beta }^2$生成器网络的分布，以更有效地捕获输入样本分布。此外，我们为判别器网络实现了简单而有效的架构，旨在为生成器提供更多的判别梯度信息。我们在四个基准测试公共数据集上的实验结果证实了我们的 GAN 在似然适应度指标和机器学习效率方面的卓越性能。