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.
Ambiguous annotations: When is a pedestrian not a pedestrian? InFirst Vision and Language for Autonomous Driving and Robotics Workshop, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.