Neighborhood-Regularized Self-Training for Learning with Few Labels.
Proc AAAI Conf Artif Intell
; 37(9): 10611-10619, 2023 Jun 27.
Article
en En
| MEDLINE
| ID: mdl-38333625
ABSTRACT
Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. We further stabilize self-training via aggregating the predictions from different rounds during sample selection. Experiments on eight tasks show that our proposed method outperforms the strongest self-training baseline with 1.83% and 2.51% performance gain for text and graph datasets on average. Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8% and saves 57.3% of the time when compared with the best baseline. Our code and appendices will be uploaded to https//github.com/ritaranx/NeST.
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Colección:
01-internacional
Base de datos:
MEDLINE
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En
Revista:
Proc AAAI Conf Artif Intell
Año:
2023
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Article