A meta-learned embedding with an EM-adapted Gaussian mixture and annotator confusion matrices improves few-shot classification from multiple noisy annotators.
Here, methods with the symbol ‘MV’ used majority voting for determining the label of each support example
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Meta-learning Representations for Learning from Multiple Annotators
A meta-learned embedding with an EM-adapted Gaussian mixture and annotator confusion matrices improves few-shot classification from multiple noisy annotators.