21 citations · 45 across the 7 of their papers we have counts for
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cs.CV2022★ 21 cited
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Ye Du, Yujun Shen, Haochen Wang +6
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…
cs.SD2022★ 2 cited
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition
Ye-Qian Du, Jie Zhang, Qiu-Shi Zhu +4
Unpaired data has shown to be beneficial for low-resource automatic speech recognition~(ASR), which can be involved in the design of hybrid models with multi-task training or langu…