3 papers
cs.CV2021
Fusion of evidential CNN classifiers for image classification
Zheng Tong, Philippe Xu, Thierry Denoeux
We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extra…
cs.AI2021
An evidential classifier based on Dempster-Shafer theory and deep learning
Zheng Tong, Philippe Xu, Thierry Denœux
We propose a new classifier based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification. In this classifier, called the…
cs.CV2021
Evidential fully convolutional network for semantic segmentation
Zheng Tong, Philippe Xu, Thierry Denœux
We propose a hybrid architecture composed of a fully convolutional network (FCN) and a Dempster-Shafer layer for image semantic segmentation. In the so-called evidential FCN (E-FCN…