4 papers · 1 filter
Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning
Jiaqi Wu, Junbiao Pang, Qingming Huang
Current Semi-supervised Learning (SSL) adopts the pseudo-labeling strategy and further filters pseudo-labels based on confidence thresholds. However, this mechanism has notable dra…
Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning
Jiaqi Wu, Junbiao Pang, Qingming Huang
In computer vision, traditional ensemble learning methods exhibit either a low training efficiency or the limited performance to enhance the reliability of deep neural networks. In…
Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression
Jiaqi Wu, Junbiao Pang, Qingming Huang
Both semi-supervised classification and regression are practically challenging tasks for computer vision. However, semi-supervised classification methods are barely applied to regr…
Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation
Jiaqi Wu, Junbiao Pang, Qingming Huang
Semi-supervised pose estimation is a practically challenging task for computer vision. Although numerous excellent semi-supervised classification methods have emerged, these method…