How does the scalability of diffusion-based tabular generative models compare to CTGAN in terms of training ti
Abstract
Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data. Studies involving m Research goal: How does the scalability of diffusion-based tabular generative models compare to CTGAN in terms of training time and memory usage when generating synthetic data for the GLUE benchmark? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.
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