Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus-Reference-Cited by-同舟云学术

Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus

Published:2022 Issue: Volume:30 Page:2352-2361
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Liu Zhe¹^ORCID,Li Yun²^ORCID,Yao Lina²^ORCID,Lucas Molly³,Monaghan Jessica J. M.⁴,Zhang Yu⁵

Affiliation:

1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China

2. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia

3. Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA

4. National Acoustic Laboratories, The Australian Hearing Hub, Macquarie University, Sydney, NSW, Australia

5. Department of Bioengineering, Lehigh University, Bethlehem, PA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/9695946/09864608.pdf?arnumber=9864608

Reference59 articles.

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3. Attribute-Modulated Generative Meta Learning for Zero-shot Learning

4. Disentangled and side-aware unsupervised domain adaptation for cross-dataset subjective tinnitus diagnosis;liu;arXiv 2205 03230,2022

5. EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals;gregor hartmann;arXiv 1806 01875,2018

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