Web: http://arxiv.org/abs/2209.06993

Sept. 16, 2022, 1:15 a.m. | Ye Du, Yujun Shen, Haochen Wang, Jingjing Fei, Wei Li, Liwei Wu, Rui Zhao, Zehua Fu, Qingjie Liu

cs.CV updates on arXiv.org arxiv.org

Self-training has shown great potential in semi-supervised learning. Its core
idea is to use the model learned on labeled data to generate pseudo-labels for
unlabeled samples, and in turn teach itself. To obtain valid supervision,
active attempts typically employ a momentum teacher for pseudo-label prediction
yet observe the confirmation bias issue, where the incorrect predictions may
provide wrong supervision signals and get accumulated in the training process.
The primary cause of such a drawback is that the prevailing self-training
framework …

arxiv framework future segmentation self-training semantic training

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