Tag: semi-supervised learning

Researchers have proposed PSI-SAD-DA, a post-selection inference framework that provides valid p-values and false positive rate control for semi-supervised anomaly ...

A new graph neural network framework called EGNN expands rare attack samples and uses threshold-controlled pseudo-labeling to improve intrusion detection ...

A new open-access study reports that fusing attribute features with graph structure and training classifiers collaboratively reached 0.724 accuracy on ...

Researchers report a semi-supervised framework combining pseudo-labeling, active learning, and ensemble methods that detects fluent hallucinations in large language model ...

The CARD framework reached a Macro-AUROC of 75.9% in semi-supervised chest X-ray disease classification, rising to 77.0% when pre-diagnostic clinical ...

A semi-supervised framework that guides segmentation networks with signed distance maps achieved stronger boundary delineation on two breast cancer histopathology ...

A semi-supervised YOLO11 pipeline using confidence filtering, DIoU-NMS deduplication, and light manual checks outperforms fully supervised scrap steel detection while ...

A semi-supervised adversarial autoencoder detects gait phases in real time for post-stroke exoskeleton control, reaching 93.79% accuracy with only 10% ...

Researchers have built a federated learning framework for intelligent transportation that combines semi-supervised pseudo-labeling, zk-SNARK update verification and blockchain auditing ...

Researchers have developed a semi-supervised, domain-adaptive intrusion detection algorithm that combines mutual-information-maximizing adversarial networks with uncertainty-aware pseudo-labeling to detect attacks ...