Mean Teacher

The Mean Teacher framework is a semi-supervised learning technique that leverages a "teacher" model, an exponential moving average of student model parameters, to improve the training of a "student" model using both labeled and unlabeled data. Current research focuses on enhancing Mean Teacher's robustness and applicability across diverse tasks, including object detection, semantic segmentation, and cross-domain adaptation, often addressing challenges like noisy pseudo-labels and domain shifts through techniques such as confidence alignment and dynamic updating. This approach holds significant promise for improving the efficiency and performance of deep learning models in scenarios with limited labeled data, impacting fields ranging from medical image analysis to natural language processing.