Interpretability Benefits of Uncertainty Quantification
Interpretability Benefits of Uncertainty Quantification
We use a simple modification of a classical network inference using Monte Carlo dropout to estimate uncertainty. We characterize sources of uncertainty to proxy calibration and disambiguate annotator and data bias.
Publications: ICCVW'19
Awards: MIT Stephen A. Schwarzman College of Computing, Machine Learning Across Disciplines Challenge
More: Code