Transferability Metric

Transferability metrics aim to efficiently predict the success of transferring a pre-trained model to a new task, avoiding costly trial-and-error fine-tuning. Current research focuses on developing metrics applicable across diverse tasks (e.g., image classification, object detection, semantic segmentation, multi-label ECG diagnosis) and model architectures, often leveraging techniques like optimal transport and Fisher discriminant analysis to assess model suitability. While some metrics show promising correlations with actual performance in specific contexts, consistent, universally reliable metrics remain elusive, highlighting the need for further research to improve their robustness and generalizability across different domains and data characteristics. Improved transferability metrics would significantly accelerate the development and deployment of machine learning models, particularly in data-scarce applications.