#pseudo-labeling News & Analysis

2 articles tagged with #pseudo-labeling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Feb 277/108

🧠

A Confidence-Variance Theory for Pseudo-Label Selection in Semi-Supervised Learning

Researchers introduce a Confidence-Variance (CoVar) theory framework that improves pseudo-label selection in semi-supervised learning by combining maximum confidence with residual-class variance. The method addresses overconfidence issues in deep networks and demonstrates consistent improvements across multiple datasets including PASCAL VOC, Cityscapes, CIFAR-10, and Mini-ImageNet.

$NEAR

AINeutralarXiv – CS AI · Apr 136/10

🧠

GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback

Researchers propose GNN-as-Judge, a framework combining Large Language Models with Graph Neural Networks to improve learning on text-attributed graphs in low-resource settings. The approach uses collaborative pseudo-labeling and weakly-supervised fine-tuning to generate reliable labels while reducing noise, demonstrating significant performance gains when labeled data is scarce.