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A Comparative Survey of Deep Active Learning. (arXiv:2203.13450v1 [cs.LG])
March 28, 2022, 1:11 a.m. | Xueying Zhan, Qingzhong Wang, Kuan-hao Huang, Haoyi Xiong, Dejing Dou, Antoni B. Chan
cs.LG updates on arXiv.org arxiv.org
Active Learning (AL) is a set of techniques for reducing labeling cost by
sequentially selecting data samples from a large unlabeled data pool for
labeling. Meanwhile, Deep Learning (DL) is data-hungry, and the performance of
DL models scales monotonically with more training data. Therefore, in recent
years, Deep Active Learning (DAL) has risen as feasible solutions for
maximizing model performance while minimizing the expensive labeling cost.
Abundant methods have sprung up and literature reviews of DAL have been
presented before. …
More from arxiv.org / cs.LG updates on arXiv.org
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