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

6 Feb 2022  ·  Guy Hacohen, Avihu Dekel, Daphna Weinshall ·

Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable querying strategies. Our theoretical analysis shows a behavior reminiscent of phase transition: typical examples are best queried when the budget is low, while unrepresentative examples are best queried when the budget is large. Combined evidence shows that 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 a variety of architectures and image datasets, TypiClust outperforms all other active learning strategies in the low-budget regime. Using TypiClust in the semi-supervised framework, performance gets an even more significant boost. In particular, state-of-the-art semi-supervised methods trained on CIFAR-10 with 10 labeled examples selected by TypiClust, reach 93.2% accuracy -- an improvement of 39.4% over random selection. Code is available at

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Active Learning CIFAR10 (10,000) TypiClust Accuracy 93.2 # 1


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