Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems

ArXi:2501.00277v2 Announce Type: replace-cross Modern AI algorithms require labeled data. In real world, majority of data are unlabeled. Labeling the data are costly. this is particularly true for some areas requiring special skills, such as reading radiology images by physicians. To most efficiently use expert's time for the data labeling, one promising approach is human-in-the-loop active learning algorithm. In this work, we propose a novel active learning framework with significant potential for application in modern AI systems.