Neighborhood-Regularized Self-Training for Learning with Few Labels
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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 2.51 analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8 the best baseline. Our code and appendices will be uploaded to https://github.com/ritaranx/NeST.
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