Bridging Domain Expertise and Generalization for Performance Estimation
Researchers propose FRAP (Fused Reference Alignment Prediction), a method that combines a foundation model with a domain-specific base model to improve performance estimation when AI models encounter distribution shifts. By aligning and fusing predictions from both models through calibration, FRAP provides more reliable performance indicators without ground-truth labels.