A meta-learned embedding with an EM-adapted Gaussian mixture and annotator confusion matrices improves few-shot classification from multiple noisy annotators.
The number of classes in each task is four, and the number of support data per class (shot) was one, three, and five
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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.