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.

翻译：条件性已成为生成合成图像的基因反向网络(GANs)的核心组成部分。 GANs通常使用潜在条件来控制生成过程。 然而, 列表数据只包含明显的变量。 因此, 潜在条件性要么限制了生成的数据, 要么没有产生足够好的结果。 因此, 我们提出一种新的方法, 将受图像完成方法启发的表单 GAN 中的条件性包含在内。 文章展示了 ciDATGAN, 即直接循环的表层GAN (DATGAN) 的演进, 已经显示为超前状态的表式GAN 模型。 首先, 我们显示, 附加条件性投入会阻碍模型相对于其前身的性能。 然后, 我们证明 ciDATGAN 可以在精选的有条件输入帮助下用于不偏差的数据集。 最后, 它表明 ciDATGAN 可以学习数据背后的逻辑, 从而用来使用小的种子数据集完成大型合成数据集 。