An improved tabular data generator with VAE-GMM integration

Understand

The rising use of machine learning in various fields requires robust methods to create synthetic tabular data.

• Data should preserve key characteristics while addressing data scarcity challenges.
• Current approaches based on Generative Adversarial Networks, such as the state-of-the-art CTGAN model, struggle with the complex structures inherent in tabular data.
• These data often contain both continuous and discrete features with non-Gaussian distributions.