Semisupervised learning (SSL) methods have been proved to be effective at solving the
labeled samples shortage problem by using a large number of unlabeled samples together …

Semi-supervised learning is proposed to exploit both labeled and unlabeled data. However,
as the scale of data in real world applications increases significantly, conventional semi …

To utilize both labeled data and unlabeled data in real-world applications, semi-supervised
learning is widely used as an effective technique. However, most semi-supervised methods …

Semisupervised learning (SSL) has been widely used in numerous practical applications
where the labeled training examples are inadequate while the unlabeled examples are …

Semi-supervised learning (SSL) methods, which exploit both the labeled and unlabeled
data, have attracted a lot of attention. One of the major categories of SSL methods, graph …

Semi-supervised learning (SSL) suffers from severe performance degradation when labeled
and unlabeled data come from inconsistent data distributions. However, there is still a lack of …

Semi-supervised learning (SSL) has proven to be successful in overcoming labeling
difficulties by leveraging unlabeled data. Previous SSL algorithms typically assume a …

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 a fundamental challenge in machine learning for
decades. The primary family of SSL algorithms, known as pseudo-labeling, involves …

Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …