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：随着当前对表格数据关注度的日益提升，将合成表格应用于各类任务的研究已扩展至多种场景。得益于生成式建模的最新进展，由表格数据合成模型生成的虚假数据已变得复杂且逼真。然而，在建模表格数据的离散变量（列）时仍存在困难。本研究提出通过两个扩散模型分别处理连续变量与离散变量（但两者相互以条件约束）。这两个扩散模型在训练过程中通过相互读取条件实现协同演化。为进一步增强扩散模型间的耦合，我们引入了一种结合负采样方法的对比学习技术。在11个真实表格数据集和8种基线方法的实验中，我们验证了所提出方法CoDi的有效性。