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

TL;DR

This paper explores how active learning strategies should adapt to different budget sizes, introducing TypiClust, a new method that excels in low-budget scenarios and significantly improves semi-supervised learning performance.

Contribution

The paper presents TypiClust, a novel deep active learning strategy optimized for low budgets, supported by theoretical analysis and empirical validation across multiple datasets.

Findings

TypiClust outperforms existing strategies in low-budget regimes.

Using TypiClust in semi-supervised learning greatly enhances accuracy.

State-of-the-art results achieved with minimal labeled data on CIFAR-10.

Abstract

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 https://github.com/avihu111/TypiClust.