Active Learning with Multiple Annotators

Multi-annotator Query-by-Committee

Extension of Query-by-Committee where multiple models are trained on different annotation subsets to identify samples with the greatest predictive disagreement. This approach is enriched by the diversity of annotator perspectives within the committee.

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