VisGIL: machine learning-based visual guidance for interactive labeling

Abstract

AbstractLabeling of datasets is an essential task for supervised and semi-supervised machine learning. Model-based active learning and user-based interactive labeling are two complementary strategies for this task. We propose VisGIL which, using visual cues, guides the user in the selection of instances to label based on utility measures deduced from an active learning model. We have implemented the approach and conducted a qualitative and quantitative user study and a think-aloud test. The studies reveal that guidance by visual cues improves the trained model’s accuracy, reduces the time needed to label the dataset, and increases users’ confidence while selecting instances. Furthermore, we gained insights regarding how guidance impacts user behavior and how the individual visual cues contribute to user guidance. A video of the approach is available: https://ml-and-vis.org/visgil/.