Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentation

The aim of Active Learning is to select the most informative samples from an unlabelled set of data. This is useful in cases where the amount of data is large and labelling is expensive, such as in machine vision or medical imaging. Two particularities of machine vision are first, that most of the i