Pseudo Labelling

Pseudo-labeling is a semi-supervised learning technique that leverages unlabeled data by using a model's predictions as pseudo-labels for training. Current research focuses on improving the robustness and accuracy of pseudo-labeling, addressing issues like noisy pseudo-labels and confirmation bias through methods such as cluster-aware self-training, virtual category learning, and adaptive confidence calibration. These advancements are impacting various fields, including image classification, object detection, semantic segmentation, and natural language processing, by enabling more efficient and effective model training with limited labeled data. The ultimate goal is to enhance model generalization and performance in scenarios where acquiring labeled data is expensive or difficult.