  Traditional deep neural networks (NNs) have significantly contributed to the
state-of-the-art performance in the task of classification under various
application domains. However, NNs have not considered inherent uncertainty in
data associated with the class probabilities where misclassification under
uncertainty may easily introduce high risk in decision making in real-world
contexts (e.g., misclassification of objects in roads leads to serious
accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight
uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly
model the uncertainty of class probabilities and use them for classification
tasks. An ENN offers the formulation of the predictions of NNs as subjective
opinions and learns the function by collecting an amount of evidence that can
form the subjective opinions by a deterministic NN from data. However, the ENN
is trained as a black box without explicitly considering inherent uncertainty
in data with their different root causes, such as vacuity (i.e., uncertainty
due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting
evidence). By considering the multidimensional uncertainty, we proposed a novel
uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an
out-of-distribution (OOD) detection problem. We took a hybrid approach that
combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly
train a model with prior knowledge of a certain class, which has high vacuity
for OOD samples. Via extensive empirical experiments based on both synthetic
and real-world datasets, we demonstrated that the estimation of uncertainty by
WENN can significantly help distinguish OOD samples from boundary samples. WENN
outperformed in OOD detection when compared with other competitive
counterparts.
