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Investigating active learning, we focus on the relation between the number oflabeled examples (budget size), and suitable querying strategies. Ourtheoretical analysis shows a behavior reminiscent of phase transition: typicalexamples are best queried when the budget is low, while unrepresentativeexamples are best queried when the budget is large. Combined evidence showsthat a similar phenomenon occurs in common classification models. Accordingly,we propose TypiClust -- a deep active learning strategy suited for low budgets.In a comparative empirical investigation of supervised learning, using avariety of architectures and image datasets, TypiClust outperforms all otheractive learning strategies in the low-budget regime. Using TypiClust in thesemi-supervised framework, performance gets an even more significant boost. Inparticular, state-of-the-art semi-supervised methods trained on CIFAR-10 with10 labeled examples selected by TypiClust, reach 93.2% accuracy -- animprovement of 39.4% over random selection. Code is available athttps://github.com/avihu111/TypiClust.

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets