An evidential classifier based on Dempster-Shafer theory and deep learning
TLDR
In this article, a new classifier based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification is proposed.About:
This article is published in Neurocomputing.The article was published on 2021-08-25 and is currently open access. It has received 27 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Journal ArticleDOI
A new correlation coefficient of mass function in evidence theory and its application in fault diagnosis
TL;DR: In this article, a new correlation coefficient is proposed, which can better show the relationship between mass functions, and an application of fault diagnosis is given, the effectiveness of proposed coefficient is illustrated by comparing with existing methods.
Journal ArticleDOI
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
TL;DR: In this article , an evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography and Computed Tomography (CT) images.
Book ChapterDOI
Fusion of Evidential CNN Classifiers for Image Classification
TL;DR: In this paper, an information-fusion approach based on belief functions was proposed to combine convolutional neural networks, where several pre-trained DS-based CNN architectures extract features from input images and convert them into mass functions on different frames of discernment.
Journal ArticleDOI
GCL-OSDA: Uncertainty prediction-based graph collaborative learning for open-set domain adaptation
TL;DR: Zhang et al. as mentioned in this paper proposed an unsupervised open-set domain adaptation (OSDA) classification framework using an evidential network and multi-binary classifier and considered their jointly selected samples as a pseudo-labelled sample set of an unknown class.
Book ChapterDOI
Deep Evidential Fusion Network for Image Classification
TL;DR: In this paper, a deep evidential fusion method was proposed to best utilize the belief assignment and uncertainty estimation by improving the objective function and introducing the approximation of the base rate distributions.
References
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Deep learning
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TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
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TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.
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