  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.
