Label efficiency has become an increasingly important objective in deep learning applications. Active learning aims to reduce the number of labeled examples needed to train deep networks, but the empirical performance of active learning algorithms can vary dramatically across datasets and applications. It is difficult to know in advance which active learning strategy will perform well or best in a given application. To address this, we propose the first adaptive algorithm selection strategy for deep active learning. For any unlabeled dataset, our (meta) algorithm TAILOR (Thompson ActIve Learning algORithm selection) iteratively and adaptively chooses among a set of candidate active learning algorithms. TAILOR uses novel reward functions aimed at gathering class-balanced examples. Extensive experiments in multi-class and multi-label applications demonstrate TAILOR's effectiveness in achieving accuracy comparable or better than that of the best of the candidate algorithms.

翻译：标签效率已成为深度学习应用中日益重要的目标。主动学习旨在减少训练深度网络所需的标注样本数量，但主动学习算法的实际性能在不同数据集和应用场景中差异显著。针对特定应用，预先判断哪种主动学习策略表现优异或最佳十分困难。为此，我们提出首个面向深度主动学习的自适应算法选择策略。针对任意未标注数据集，我们的（元）算法TAILOR（基于汤普森采样的主动学习算法自适应选择）能迭代式自适应地从一组候选主动学习算法中进行选择。TAILOR采用旨在收集类别平衡样本的新型奖励函数。在多分类与多标签应用场景中的大量实验表明，TAILOR在达到与候选算法中最佳算法相当或更优的准确率方面具有显著有效性。