Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

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) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been
under studied. While existing semi-supervised learning (SSL) methods are known to perform …

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 …

This review aims to examine the state of the art of semi-supervised learning (SSL)
techniques for addressing class imbalanced data. Class imbalance is inherent in many real …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

While self-training has advanced semi-supervised semantic segmentation, it severely suffers
from the long-tailed class distribution on real-world semantic segmentation datasets that …

The existing deep networks have shown excellent performance in remote sensing scene
classification (RSSC), which generally requires a large amount of class-balanced training …

Existing semi-supervised learning (SSL) algorithms typically assume class-balanced
datasets, although the class distributions of many real world datasets are imbalanced. In …