Confidence path regularization for handling label uncertainty in semi-supervised learning: use case in bipolar disorder monitoring-Reference-Cited by-同舟云学术

Confidence path regularization for handling label uncertainty in semi-supervised learning: use case in bipolar disorder monitoring

Published:2022-07-18 Issue: Volume: Page:
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Container-title:2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Author:

Kmita Kamil¹,Casalino Gabriella²,Castellano Giovanna²,Hryniewicz Olgierd¹,Kaczmarek-Majer Katarzyna¹

Affiliation:

1. Polish Academy of Sciences,Systems Research Institute,Warsaw,Poland

2. University of Bari Aldo Moro,Computer Science Dept.,Bari,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9882530/9882539/09882759.pdf?arnumber=9882759

Reference28 articles.

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3. Learning from noisy labels with deep neural networks: A survey;song,2020

4. Probabilistic End-To-End Noise Correction for Learning With Noisy Labels

5. Toward robustness against label noise in training deep discriminative neural networks;vahdat;Advances in neural information processing systems,2017

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