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

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 …

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 …

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 scarcity of labeled data is a critical obstacle to deep learning. Semi-supervised learning
(SSL) provides a promising way to leverage unlabeled data by pseudo labels. However …

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 …

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 …

Pseudo labeling and consistency regularization approaches with confidence-based
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an
effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this …