Efficacy of Bayesian Neural Networks in Active Learning
Obtaining labeled data for machine learning tasks can be prohibitively expensive. Active learning mitigates this issue by exploring the unlabeled data space and prioritizing the selection of data that can best improve the model performance. A common approach to active learning is to pick a small sam...
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Zusammenfassung: | Obtaining labeled data for machine learning tasks can be prohibitively
expensive. Active learning mitigates this issue by exploring the unlabeled data
space and prioritizing the selection of data that can best improve the model
performance. A common approach to active learning is to pick a small sample of
data for which the model is most uncertain. In this paper, we explore the
efficacy of Bayesian neural networks for active learning, which naturally
models uncertainty by learning distribution over the weights of neural
networks. By performing a comprehensive set of experiments, we show that
Bayesian neural networks are more efficient than ensemble based techniques in
capturing uncertainty. Our findings also reveal some key drawbacks of the
ensemble techniques, which was recently shown to be more effective than Monte
Carlo dropouts. |
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DOI: | 10.48550/arxiv.2104.00896 |