Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty

arXiv:2404.10980v1 Announce Type: new
Abstract: 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 …

arxiv classification cs.cv cs.lg deep learning type uncertainty