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 …

In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch
from semi-supervised classification, where the prediction of a weakly perturbed image …

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

Considering the ill-posed nature, contrastive regularization has been developed for single
image dehazing, introducing the information from negative images as a lower bound …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage
unlabeled data. In this work, we first construct a strong baseline of self-training (namely ST) …

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

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

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 …

Existing studies indicate that deep neural networks (DNNs) can eventually memorize the
label noise. We observe that the memorization strength of DNNs towards each instance is …