Hyper Evidential Deep Learning to Quantify Composite Classification
  Uncertainty
Changbin Li Kangshuo Li Yuzhe Ou Lance M. Kaplan Audun Jøsang Jin-Hee Cho Dong Hyun Jeong Feng Chen
Deep neural networks (DNNs) have been shown to perform well on exclusive,
multi-class classification tasks. However, when different classes have similar
visual features, it becomes challenging for human annotators to differentiate
them. This scenario necessitates the use of composite class labels. In this
paper, we propose a novel framework called Hyper-Evidential Neural Network
(HENN) that explicitly models predictive uncertainty due to composite class
labels in training data in the context of the belief theory called Subjective
Logic (SL). By placing a grouped Dirichlet distribution on the class
probabilities, we treat predictions of a neural network as parameters of
hyper-subjective opinions and learn the network that collects both single and
composite evidence leading to these hyper-opinions by a deterministic DNN from
data. We introduce a new uncertainty type called vagueness originally designed
for hyper-opinions in SL to quantify composite classification uncertainty for
DNNs. Our results demonstrate that HENN outperforms its state-of-the-art
counterparts based on four image datasets. The code and datasets are available
at: https://github.com/Hugo101/HyperEvidentialNN.