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 prob…