Performance of Novel Structural Fidelity Metrics versus FID in Evaluating Tabular Generative Models on Large-Scale Mixed Data

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: How do novel structural fidelity metrics perform compared to FID when evaluating tabular data generative models on large-scale datasets with mixed data types, measured by accuracy in capturing cross-domain dependencies?

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