Tabular Data Synthesis

A classical technical direction in data augmentation. It generates new synthetic tabular data by learning the statistical distribution of real data. Common methods include TVAE, CTGAN, and diffusion-model-based approaches. These methods rely on seed data as input and are widely used when real data is scarce or sensitive.