Comparative Analysis of Tabular Diffusion Models, WGANs, and VAEs on Large-Scale Imbalanced High-Dimensional Data via FID

Synthetic data are increasingly being recognized for their potential to address serious real-world challenges in various domains. They provide innovative solutions to combat the data scarcity, privacy concerns, and algorithmic biases commonly used in machine learning applications. Synthetic data preserve all underlying patterns and behaviors of the original dataset while altering the actual content. The methods proposed in the literature to generate synthetic data vary from large language models (LLMs), which are pre-trained on gigantic datasets, to generative adversarial networks (GANs) and v

Research goal: How do tabular diffusion models compare to Wasserstein GANs and VAEs in terms of FID score when scaled to larger imbalanced datasets with 100+ features?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.8/10.