2022
DOI: 10.1007/s41884-022-00081-x
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Active learning by query by committee with robust divergences

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Cited by 8 publications
(1 citation statement)
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“…This algorithm employs a committee of models (so-called students). New samples for annotation are selected based on the maximum disagreement in prediction between the student models [32][33][34]. Therefore, a normal QBC algorithm requires multiple models to be trained in parallel.…”
Section: Challenges Going From 2d To 3dmentioning
confidence: 99%
“…This algorithm employs a committee of models (so-called students). New samples for annotation are selected based on the maximum disagreement in prediction between the student models [32][33][34]. Therefore, a normal QBC algorithm requires multiple models to be trained in parallel.…”
Section: Challenges Going From 2d To 3dmentioning
confidence: 99%