An Evidential Deep Neural Network for Set-Valued Classification and Novelty Detection

This research presents an evidential deep neural network (EDNN), which is a machine learning model that combines evidence theory (a method for handling uncertainty) with convolutional neural networks (CNNs, algorithms that process images). Unlike traditional classifiers that assume all possible categories are known, the EDNN works under an open-world assumption (acknowledging that unknown categories may exist) and can classify items into single categories, multiple possible categories, or identify them as completely novel.