论文标题
CIDATGAN：表格gan的条件输入
ciDATGAN: Conditional Inputs for Tabular GANs
论文作者
论文摘要
条件性已成为生成综合图像的生成对抗网络（GAN）的核心组成部分。 GAN通常使用潜在条件来控制生成过程。但是，表格数据仅包含明显的变量。因此，潜在的条件性要么限制生成的数据，要么无法产生足够好的结果。因此，我们提出了一种新方法，以在受图像完成方法启发的表格gan中包含条件。本文介绍了Cidatgan，这是定向的无环形GAN（DATGAN）的演变，已被证明超过了最先进的表格GAN模型。首先，我们表明，与其前身相比，有条件输入的添加确实阻碍了模型的性能。然后，我们证明Cidatgan可以在精心挑选的条件输入的帮助下将其用于无偏数据集。最后，它表明Cidatgan可以学习数据背后的逻辑，因此可以使用来自较小的馈线数据集中的数据来完成大型合成数据集。
Conditionality has become a core component for Generative Adversarial Networks (GANs) for generating synthetic images. GANs are usually using latent conditionality to control the generation process. However, tabular data only contains manifest variables. Thus, latent conditionality either restricts the generated data or does not produce sufficiently good results. Therefore, we propose a new methodology to include conditionality in tabular GANs inspired by image completion methods. This article presents ciDATGAN, an evolution of the Directed Acyclic Tabular GAN (DATGAN) that has already been shown to outperform state-of-the-art tabular GAN models. First, we show that the addition of conditional inputs does hinder the model's performance compared to its predecessor. Then, we demonstrate that ciDATGAN can be used to unbias datasets with the help of well-chosen conditional inputs. Finally, it shows that ciDATGAN can learn the logic behind the data and, thus, be used to complete large synthetic datasets using data from a smaller feeder dataset.
