  There are two major types of uncertainty one can model. Aleatoric uncertainty
captures noise inherent in the observations. On the other hand, epistemic
uncertainty accounts for uncertainty in the model -- uncertainty which can be
explained away given enough data. Traditionally it has been difficult to model
epistemic uncertainty in computer vision, but with new Bayesian deep learning
tools this is now possible. We study the benefits of modeling epistemic vs.
aleatoric uncertainty in Bayesian deep learning models for vision tasks. For
this we present a Bayesian deep learning framework combining input-dependent
aleatoric uncertainty together with epistemic uncertainty. We study models
under the framework with per-pixel semantic segmentation and depth regression
tasks. Further, our explicit uncertainty formulation leads to new loss
functions for these tasks, which can be interpreted as learned attenuation.
This makes the loss more robust to noisy data, also giving new state-of-the-art
results on segmentation and depth regression benchmarks.
