  Uncertainty estimation is a key component in any deployed machine learning
system. One way to evaluate uncertainty estimation is using
"out-of-distribution" (OoD) detection, that is, distinguishing between the
training data distribution and an unseen different data distribution using
uncertainty. In this work, we show that current feature density based
uncertainty estimators cannot perform well consistently across different OoD
detection settings. To solve this, we propose to decompose the learned
representations and integrate the uncertainties estimated on them separately.
Through experiments, we demonstrate that we can greatly improve the performance
and the interpretability of the uncertainty estimation.
