An Empirical Study on the Efficacy of Deep Active Learning Techniques

YU LI, Muxi Chen, Yannan Liu, Daojing He, Qiang Xu

Keywords: deep neural networks, active learning, semi-supervised learning

TL;DR: Our paper provides a comprehensive empirical study of existing deep active learning methods.

Abstract: Deep Active Learning (DAL) has been advocated as a promising method to reduce labeling costs in supervised learning. However, existing evaluations of DAL methods are based on different settings, and their results are controversial. To tackle this issue, this paper comprehensively evaluates 19 existing DAL methods in a uniform setting, including traditional fully-\underline{s}upervised \underline{a}ctive \underline{l}earning (SAL) strategies and emerging \underline{s}emi-\underline{s}upervised \underline{a}ctive \underline{l}earning (SSAL) techniques. We have several non-trivial findings. First, most SAL methods cannot achieve higher accuracy than random selection. Second, semi-supervised training brings significant performance improvement compared to pure SAL methods. Third, performing data selection in the SSAL setting can achieve a significant and consistent performance improvement, especially with abundant unlabeled data. Our findings produce the following guidance for practitioners: one should (i) apply SSAL as early as possible and (ii) collect more unlabeled data whenever possible, for better model performance. We will release our code upon acceptance.

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