@inproceedings{3291b3ce6a7a4e34905da89cb0587d4a,
title = "A peer learning method for building robust text classification models",
abstract = "Classification is an essential task in many practical problems. A machine learning based classification model is built to minimize the error between actual labels and predicted labels generated by the model. When the model depends on only actual labels during the training, it can generate monotonous distributional predictions. In order to make a robust model, it needs to use other sources of information in addition to the original labels. To address this issue, we propose a peer learning method that enables the target model to reference multiple peer models and that can control the impact of peers on the target model during the training phase. The experiment results indicate that the proposed method is promising.",
keywords = "Classification, Deep learning, Label refinery, Peer learning",
author = "Jeon, \{Hyun Kyu\} and Cheong, \{Yun Gyung\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021 ; Conference date: 17-01-2021 Through 20-01-2021",
year = "2021",
month = jan,
doi = "10.1109/BigComp51126.2021.00069",
language = "English",
series = "Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "321--324",
editor = "Herwig Unger and Jinho Kim and U Kang and Chakchai So-In and Junping Du and Walid Saad and Young-guk Ha and Christian Wagner and Julien Bourgeois and Chanboon Sathitwiriyawong and Hyuk-Yoon Kwon and Carson Leung",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021",
}