Pseudo Labeled Sample

Pseudo-labeling is a semi-supervised learning technique that leverages unlabeled data by assigning pseudo-labels based on a model's predictions, thereby augmenting limited labeled datasets. Current research focuses on improving pseudo-label quality by addressing issues like noisy labels and imbalanced class distributions, often employing techniques such as data augmentation (e.g., Mixup variants), uncertainty estimation, and contrastive learning within various model architectures. These advancements aim to enhance the accuracy and efficiency of semi-supervised learning across diverse applications, including image classification, semantic segmentation, and natural language processing, where labeled data is scarce or expensive to obtain. The ultimate goal is to improve model performance and reduce reliance on large, fully labeled datasets.