Minimax Active Learning

Sayna Ebrahimi
Sayna Ebrahimi
William Gan
William Gan
Kamyar Salahi
Kamyar Salahi
Cited by: 0|Bibtex|Views9
Other Links: arxiv.org

Abstract:

Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either rely on model uncertainty to select the most uncertain samples or use clustering or reconstruction to choose the most diverse set of unlabeled examples. Whil...More

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