  Modeling the probability distribution of rows in tabular data and generating
realistic synthetic data is a non-trivial task. Tabular data usually contains a
mix of discrete and continuous columns. Continuous columns may have multiple
modes whereas discrete columns are sometimes imbalanced making the modeling
difficult. Existing statistical and deep neural network models fail to properly
model this type of data. We design TGAN, which uses a conditional generative
adversarial network to address these challenges. To aid in a fair and thorough
comparison, we design a benchmark with 7 simulated and 8 real datasets and
several Bayesian network baselines. TGAN outperforms Bayesian methods on most
of the real datasets whereas other deep learning methods could not.
