Controlled experiments on MNIST show human soft-labels act as a regularizer that improves calibration on hard samples and aligns model uncertainty with humans, beyond accuracy gains from correcting mislabels.
Facing uncertainty in the game of bridge: A calibration study.Organizational Behavior and Human Decision Processes, 39(1):98–114, 1987
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An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
Controlled experiments on MNIST show human soft-labels act as a regularizer that improves calibration on hard samples and aligns model uncertainty with humans, beyond accuracy gains from correcting mislabels.