423
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
arXiv:2410.01933v2 Announce Type: replace
Abstract: Synthetic tabular data generation has gained significant attention for its potential in data augmentation and privacy-preserving data sharing. While recent methods like diffusion and auto-regressive models (i.e., transformer) have advanced the fie…
Abstract: Synthetic tabular data generation has gained significant attention for its potential in data augmentation and privacy-preserving data sharing. While recent methods like diffusion and auto-regressive models (i.e., transformer) have advanced the fie…