Pseudo Labeling

Pseudo-labeling is a semi-supervised learning technique that leverages unlabeled data by using a model's predictions as pseudo-labels to augment training datasets. Current research focuses on improving the reliability of these pseudo-labels through techniques like confidence thresholding, multi-view approaches, and incorporating additional information such as contextual metadata or neighbor relations. This approach is particularly valuable in domains with limited labeled data, such as medical image analysis, speech processing, and object detection, leading to improved model performance and reduced annotation costs. The resulting advancements have significant implications for various applications where obtaining labeled data is expensive or difficult.