Semi Supervised Teacher Student

Semi-supervised teacher-student learning leverages both labeled and unlabeled data to train robust machine learning models, particularly valuable when labeled data is scarce. Current research focuses on improving pseudo-label generation accuracy, addressing class imbalance issues, and exploring efficient knowledge transfer mechanisms within teacher-student frameworks, often employing transformer architectures or reinforcement learning to guide the process. This approach significantly enhances model performance across various domains, including image recognition, music information retrieval, and dialogue analysis, by improving generalization and reducing the reliance on expensive data annotation.