146 citations · 173 across the 4 of their papers we have counts for
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cs.CV2022★ 2 cited
Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound
Feiyu Wang, Qin Wang, Wen Li +2
In this work, we revisit the semi-supervised learning (SSL) problem from a new perspective of explicitly reducing empirical distribution mismatch between labeled and unlabeled samp…
cs.CV2021★ 2 cited
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Qin Wang +2
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
cs.CV2019
Semi-Supervised Learning by Augmented Distribution Alignment
Qin Wang, Wen Li, Luc Van Gool
In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-…