Auditing Metrics for Synthetic Data Utility: A Cross-Domain Study of Appearance and Geometry
SADGE is a quantitative metric designed to predict how useful synthetic image datasets will be for real-world computer vision tasks before any model training occurs. The study found that neither appearance nor geometry alone is sufficient; the best performance comes from combining them using a specific bilinear formula. This fused metric strongly correlates with downstream task success across various benchmarks.