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

TL;DR

This paper introduces a novel human-in-the-loop active learning framework that intelligently combines multiple query schemes to improve data labeling efficiency and accuracy in AI systems.

Contribution

It presents a new active learning framework that integrates different query types and automatically determines optimal questions, enhancing label efficiency and model performance.

Findings

Framework achieves higher accuracy than existing methods.

Lower loss observed across five real-world datasets.

Effective on complex image labeling tasks.

Abstract

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. Unlike the traditional active learning methods, which only focus on determining which data point should be labeled, our framework also introduces an innovative perspective on incorporating different query scheme. We propose a model to integrate the information from different types of queries. Based on this model, our active learning frame can automatically determine how the next question is queried. We further developed a data driven exploration and exploitation framework into our active learning method. This method can be embedded in numerous active learning algorithms. Through simulations on five real-world datasets, including a highly complex real image task, our proposed active learning framework exhibits higher accuracy and lower loss compared to other methods.