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Title: Simple and Effective Approaches for Uncertainty Prediction in Facial Action Unit Intensity Regression
Knowing how much to trust a prediction is important for many critical applications. We describe two simple approaches to estimate uncertainty in regression prediction tasks and compare their performance and complexity against popular approaches. We operationalize uncertainty in regression as the absolute error between a model's prediction and the ground truth. Our two proposed approaches use a secondary model to predict the uncertainty of a primary predictive model. Our first approach leverages the assumption that similar observations are likely to have similar uncertainty and predicts uncertainty with a non-parametric method. Our second approach trains a secondary model to directly predict the uncertainty of the primary predictive model. Both approaches outperform other established uncertainty estimation approaches on the MNIST, DISFA, and BP4D+ datasets. Furthermore, we observe that approaches that directly predict the uncertainty generally perform better than approaches that indirectly estimate uncertainty.
Authors:
;
Award ID(s):
1750439 1722822 1734868
Publication Date:
NSF-PAR ID:
10169266
Journal Name:
Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition
Volume:
1
Page Range or eLocation-ID:
304-308
Sponsoring Org:
National Science Foundation
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