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Neural networks are ubiquitous in many tasks, but trusting their predictionsis an open issue. Uncertainty quantification is required for many applications,and disentangled aleatoric and epistemic uncertainties are best. In this paper,we generalize methods to produce disentangled uncertainties to work withdifferent uncertainty quantification methods, and evaluate their capability toproduce disentangled uncertainties. Our results show that: there is aninteraction between learning aleatoric and epistemic uncertainty, which isunexpected and violates assumptions on aleatoric uncertainty, some methods likeFlipout produce zero epistemic uncertainty, aleatoric uncertainty is unreliablein the out-of-distribution setting, and Ensembles provide overall the bestdisentangling quality. We also explore the error produced by the number ofsamples hyper-parameter in the sampling softmax function, recommending N > 100samples. We expect that our formulation and results help practitioners andresearchers choose uncertainty methods and expand the use of disentangleduncertainties, as well as motivate additional research into this topic.

A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement