Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw
data utilization. However, its training procedure suffers from confirmation bias due to the …

Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised
learning (SSL). It relies on including high-confidence predictions made on unlabeled data as …

The recent research in semi-supervised learning (SSL) is mostly dominated by consistency
regularization based methods which achieve strong performance. However, they heavily …

Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on
iteratively using the model to generate artificial labels for the unlabeled data to train against …

The capability of the traditional semi-supervised learning (SSL) methods is far from real-
world application due to severely biased pseudo-labels caused by (1) class imbalance and …

The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the
limited labeled data and massive unlabeled data to improve the model's generalization …

Semi-supervised learning (SSL) has attracted enormous attention due to its vast potential of
mitigating the dependence on large labeled datasets. The latest methods (eg, FixMatch) use …

Deep learning is pushing the state-of-the-art in many computer vision applications.
However, it relies on large annotated data repositories, and capturing the unconstrained …

In semi-supervised learning, unlabeled samples can be utilized through augmentation and
consistency regularization. However, we observed certain samples, even undergoing strong …

Learning with few labeled data has been a longstanding problem in the computer vision and
machine learning research community. In this paper, we introduced a new semi-supervised …