Adaptive Pseudo

Adaptive pseudo-labeling is a semi-supervised learning technique aiming to improve model performance by leveraging unlabeled data through iteratively refined pseudo-labels. Current research focuses on enhancing pseudo-label quality via methods like adaptive label quality assessment, dynamic selection modules based on class activation maps or consistency measures, and class-aware strategies to address class imbalance and improve reliability. These advancements are significantly impacting various fields, including medical image analysis, object detection, and temporal action localization, by reducing the need for extensive manual annotation and improving model generalization in data-scarce scenarios.