With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis models become sophisticated and realistic. However, there still exists a difficulty in modeling discrete variables (columns) of tabular data. In this work, we propose to process continuous and discrete variables separately (but being conditioned on each other) by two diffusion models. The two diffusion models are co-evolved during training by reading conditions from each other. In order to further bind the diffusion models, moreover, we introduce a contrastive learning method with a negative sampling method. In our experiments with 11 real-world tabular datasets and 8 baseline methods, we prove the efficacy of the proposed method, called CoDi.

翻译：随着近年来对表格数据的日益关注，将合成表格应用于各种任务的研究已扩展至多个场景。得益于生成建模的最新进展，由表格数据合成模型生成的假数据变得精细且逼真。然而，对表格数据中的离散变量（列）进行建模仍存在困难。在本工作中，我们提出通过两个扩散模型分别处理连续变量和离散变量（但彼此相互条件化）。这两个扩散模型在训练过程中通过读取彼此的条件实现共进化。为了进一步绑定扩散模型，我们引入了一种结合负采样方法的对比学习方法。在基于11个真实世界表格数据集和8种基线方法的实验中，我们验证了所提方法（命名为CoDi）的有效性。