Comparative Performance of VAEs, GANs, and Diffusion Models for Imbalanced Tabular Data in Downstream Classification

Generative artificial intelligence (AI) has emerged as a powerful technology with numerous applications in various domains. There is a need to identify the requirements and evaluation metrics for generative AI models designed for specific tasks. The purpose of the research aims to investigate the fundamental aspects of generative AI systems, including their requirements, models, input--output formats, and evaluation metrics. The study addresses key research questions and presents comprehensive insights to guide researchers, developers, and practitioners in the field. Firstly, the requirements n

Research goal: How does the performance of VAEs and GANs in synthetic data generation for imbalanced tabular data compare to diffusion models when evaluated on downstream classification tasks using metrics like F1-score and AUC-ROC?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.2/10.