Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning

• URL: http://arxiv.org/abs/2106.12059v3
• Date: Tue, 19 Sep 2023 21:20:38 GMT
• Title: Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
• Authors: Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frederic Branchaud-Charron, Yarin Gal
• Abstract summary: We examine a simple strategy for adapting well-known single-point acquisition functions to allow batch active learning. This strategy can 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.
• Score: 48.19646855997791
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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?

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