Leonidas Akritidis

A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces

The evaluation of ctdGAN with 14 imbalanced datasets demonstrated its strong ability in generating high fidelity samples and improving classification accuracy, and several other improvements, including a simple, yet effective cluster-wise scaling technique that captures multiple feature modes without affecting data dimensionality.