DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition

This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e. , the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 30; S. 9208 - 9219
Hauptverfasser:	Xie, Jiyang, Ma, Zhanyu, Xue, Jing-Hao, Zhang, Guoqiang, Sun, Jian, Zheng, Yinhe, Guo, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Bayes methods Classifiers Deep learning dual supervised framework Feature extraction Image recognition Inference mixture of gaussian mixture models Object recognition Optimization Probabilistic logic Probabilistic models Statistical methods Stochastic processes Uncertainty uncertainty inference
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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