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 …

In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

As a mainstream framework in the field of semi-supervised learning (SSL), self-training via
pseudo labeling and its variants have witnessed impressive progress in semi-supervised …

Abstract Semi-Supervised Learning (SSL) has been an effective way to leverage abundant
unlabeled data with extremely scarce labeled data. However, most SSL methods are …

In an effort to further advance semi-supervised generative and classification tasks, we
propose a simple yet effective training strategy called* dual pseudo training*(DPT), built …

Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are
expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled …

The challenge in sourcing attribution for forgery faces has gained widespread attention due
to the rapid development of generative techniques. While many recent works have taken …

The critical challenge of semi-supervised semantic segmentation lies in how to fully exploit a
large volume of unlabeled data to improve the model's generalization performance for …

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 …

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 …