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

Summary

The paper proposes the evidential deep neural network (EDNN), a convolutional neural network model built on evidence theory that performs set-valued classification and novelty detection in one framework under the open-world assumption. Its generalized belief classifier module assigns basic belief to open sets, and its expected utility decision module chooses among single known classes, set-valued classes and a novelty class. Experiments on several benchmark datasets and the MTARSI aircraft target recognition task report strong performance on precise classification, set-valued classification and novelty detection.