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Labeling 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.

Combining Active Learning and user guidance