6 citations · 8 across the 5 of their papers we have counts for
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cs.CV2023
Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning
Guan Gui, Zhen Zhao, Lei Qi +3
In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong aug…
cs.CV2023★ 1 cited
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
Zekun Li, Lei Qi, Yinghuan Shi +1
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled…
cs.CV2023★ 6 cited
DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi
Deep Neural Networks have exhibited considerable success in various visual tasks. However, when applied to unseen test datasets, state-of-the-art models often suffer performance de…