Towards Trustworthy Multi-Label Sewer Defect Classification via Evidential Deep Learning-Reference-Cited by-同舟云学术

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Towards Trustworthy Multi-Label Sewer Defect Classification via Evidential Deep Learning

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Zhao Chenyang¹,Hu Chuanfei²,Shao Hang³,Wang Zhe²,Wang Yongxiong²

Affiliation:

1. Southeast University,The College of Software Engineering,Nanjing,China

2. University of Shanghai for Science and Technology,School of Optical-Electrical and Computer Engineering,Shanghai,China

3. Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China

Funder

Natural Science Foundation of Shanghai

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096569.pdf?arnumber=10096569

Reference23 articles.

1. Evidential deep learning to quantify classification uncertainty;sensoy;Advances in NIPS,2018

2. Toward Purifying Defect Feature for Multilabel Sewer Defect Classification

3. Multi-label out-of-distribution detection via exploiting sparsity and co-occurrence of labels

4. Trusted Multi-View Classification With Dynamic Evidential Fusion

5. Subjective Logic

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1. Energy Constrained Multi-Agent Reinforcement Learning for Coverage Path Planning;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

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