Reconceptualizing Active Learning: A Refined Framework for Methods, Frontiers, and Applications

A refined methodological framework for active learning (AL) techniques is proposed through a systematic review of conventional categorization and recent developments. The proposed framework primarily integrates sample-aware methods and metric-based methods, to offer enhanced practical utility for real-world applications. Furthermore, we review and examine emerging research frontiers focusing on three key aspects: transferability, robustness, and computational efficiency. The study also investigates diverse application scenarios from both statistical and engineering perspectives. This work serves to (a) synthesize recent advancements in the field, and (b) provide exemplars and actionable guidelines to facilitate promising research directions.