Evaluation of Tabular Data Metrics for Multimodal Generative Models in Hybrid Datasets

Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique challenges due to structural complexity, large-scale variability, and mixed data types, making it difficult to intuitively capture intricate patterns. Existing evaluation metrics offer only partial insights, lacking a comprehensive measure of generative performance. To address this limitation, we propose three novel evaluation metrics: FAED, FPCAD, and RFIS. Our extensive experimental analysis, conducted on three stan

Research goal: Can the proposed tabular data metrics be extended to evaluate multimodal generative models, and how does their robustness compare to existing metrics when applied to hybrid tabular-image datasets?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.