Active Learning Through a Covering Lens

Deep active learning aims to reduce the annotation cost for deep neural networks, which are notoriously data-hungry. Until recently, deep active learning methods struggled in the low-budget regime, where only a small amount of samples are annotated. The situation has been alleviated by recent advances in self-supervised representation learning methods, which impart the geometry of the data representation with rich information about the points. Taking advantage of this progress, we study the problem of subset selection for annotation through a "covering" lens, proposing ProbCover -- a new active learning algorithm for the low budget regime, which seeks to maximize Probability Coverage. We describe a dual way to view our formulation, from which one can derive strategies suitable for the high budget regime of active learning, related to existing methods like Coreset. We conclude with extensive experiments, evaluating ProbCover in the low budget regime. We show that our principled active learning strategy improves the state-of-the-art in the low-budget regime in several image recognition benchmarks. This method is especially beneficial in semi-supervised settings, allowing state-of-the-art semi-supervised methods to achieve high accuracy with only a few labels.