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We examine a simple stochastic strategy for adapting well-known single-pointacquisition functions to allow batch active learning. Unlike acquiring thetop-K points from the pool set, score- or rank-based sampling takes intoaccount that acquisition scores change as new data are acquired. This simplestrategy for adapting standard single-sample acquisition strategies can evenperform just as well as compute-intensive state-of-the-art batch acquisitionfunctions, like BatchBALD or BADGE, while using orders of magnitude lesscompute. In addition to providing a practical option for machine learningpractitioners, the surprising success of the proposed method in a wide range ofexperimental settings raises a difficult question for the field: when are theseexpensive batch acquisition methods pulling their weight?

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning