Active Learning with Multiple Annotators

Multi-source Label Aggregation

Fusion technique combining labels from multiple annotators into a single consensus prediction, using methods such as weighted voting, Dawid-Skene models, or Bayesian inference. This aggregation corrects individual errors and produces more reliable labels.

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