Synthesizing Tabular Data using Generative Adversarial Networks

Understand

Generative adversarial networks (GANs) implicitly learn the probability distribution of a dataset and can draw samples from the distribution.

• This paper presents, Tabular GAN (TGAN), a generative adversarial network which can generate tabular data like medical or educational records.
• Using the power of deep neural networks, TGAN generates high-quality and fully synthetic tables while simultaneously generating discrete and continuous variables.
• When we evaluate our model on three datasets, we find that TGAN outperforms conventional statistical generative models in both capturing the correlation between columns and scaling up for large datasets.