We do support active learning. With machine learning models as part of the labeling workflow, you can expect a reduction in the number of samples to label to achieve the same performance by up to 50%. This number will depend on the dataset and the task, of course.In a demo use case with medical image classification, we experienced an increase from 78% to 85% in accuracy with the same number of samples and a 30% reduction in the number of samples needed to reach 77% accuracy. This makes Kili Technology one of the most popular image annotation tools.