Tabular GANs for uneven distribution

ArXi:2010.00638v2 Announce Type: replace-cross Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular data generation, recent advances in diffusion models and large language models (LLMs) have opened new paradigms with complementary strengths in sample quality, privacy, and controllability. In this paper, we survey the landscape of tabular data generation across three major paradigms - GANs, diffusion models, and LLMs - and.