Robust Pseudo Label

Robust pseudo-labeling focuses on improving the accuracy and reliability of pseudo-labels generated for unlabeled data in semi-supervised learning, thereby enhancing model performance with limited labeled examples. Current research emphasizes techniques like consensus-based labeling from multiple models, noise calibration methods, and neighbor relation learning to mitigate the impact of noisy pseudo-labels, often within frameworks incorporating graph-based approaches or Bayesian neural networks. This area is crucial for advancing various applications, including low-resource language processing, medical image analysis, and object detection in autonomous driving, where acquiring large labeled datasets is expensive or impractical.