Web: http://arxiv.org/abs/2002.09564

June 17, 2022, 1:13 a.m. | Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, Shadab Khan

cs.CV updates on arXiv.org arxiv.org

Active learning (AL) is a promising ML paradigm that has the potential to
parse through large unlabeled data and help reduce annotation cost in domains
where labeling data can be prohibitive. Recently proposed neural network based
AL methods use different heuristics to accomplish this goal. In this study, we
demonstrate that under identical experimental settings, different types of AL
algorithms (uncertainty based, diversity based, and committee based) produce an
inconsistent gain over random sampling baseline. Through a variety of
experiments, …

active learning arxiv learning lg networks neural neural networks

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