Pseudo Label

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 accuracy and reliability of these pseudo-labels, addressing issues like class imbalance and noise through methods such as thresholding, contrastive learning, and teacher-student model architectures. This technique is significant because it allows for training high-performing models with limited labeled data, impacting various applications including object detection, image classification, and medical image segmentation.