Generalization Metric

Generalization metrics aim to quantify a machine learning model's ability to accurately predict outcomes on unseen data, a crucial aspect for reliable model deployment. Current research focuses on developing practical metrics that correlate well with real-world performance, particularly addressing the calibration of predicted probabilities and the handling of out-of-distribution data, exploring various model architectures including deep neural networks, vision-language models, and quantum-inspired generative models. These efforts are vital for improving model selection, benchmarking, and ultimately building more trustworthy and robust AI systems across diverse applications.