  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. Our implementation of TAILOR is open-sourced at
https://github.com/jifanz/TAILOR.
