8c64bc3f7796d31caa7c3e6b969bf7da-Paper-Conference.pdf

Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry. Until recently, deep active learning methods were ineffectual inthelow-budgetregime, where only asmall number ofexamples areannotated. Thesituation hasbeen alleviated byrecent advances inrepresentation andself-supervised learning, which impart thegeometry ofthe data representation with rich information about the points.