Unlabeled Sample

Unlabeled sample utilization is a crucial area in machine learning research, aiming to leverage the abundance of unlabeled data to improve model performance, especially when labeled data is scarce or expensive. Current research focuses on developing methods that effectively incorporate unlabeled samples into various learning paradigms, including semi-supervised learning, positive-unlabeled learning, and domain adaptation, often employing techniques like contrastive learning, generative adversarial networks, and consistency regularization. These advancements are significant because they address the limitations of traditional supervised learning, enabling more efficient and robust models across diverse applications, from image classification and anomaly detection to biological sequence analysis and fairness-aware machine learning.