Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning

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

Authors

• Andreas Kirsch
• Sebastian Farquhar DeepMind, United Kingdom of Great Britain and Northern Ireland
• Parmida Atighehchian
• Andrew Jesson
• Frédéric Branchaud-Charron
• Yarin Gal

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