Transferability Measure

Transferability measurement aims to efficiently predict a pre-trained model's performance on a new task *before* computationally expensive fine-tuning, thus optimizing transfer learning workflows. Recent research focuses on developing accurate and generalizable metrics, moving beyond simple discrimination measures to incorporate concepts like intra-class feature variance and PAC-Bayesian bounds. These advancements improve model selection across diverse domains, including computer vision and natural language processing, by providing more reliable quantitative evidence than relying solely on intuition or heuristics. This leads to more efficient use of resources and potentially better performance in downstream applications.