Tobespecific, our 10 meta-testdatasetsinclude Histology, Drawing, Dessert, Chinese Characters, Speed Limit Signs, Alienvs Predator, Gemstones, and Dog Breeds. Thusweuse Mean Squared Error (MSE) scores.

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.