Multiple Teacher

Multiple Teacher (MT) learning is a machine learning paradigm where a student model learns from multiple teacher models, each potentially possessing unique strengths or perspectives. Current research focuses on leveraging MT frameworks for improved semi-supervised learning, domain adaptation, and test-time adaptation, often employing techniques like knowledge distillation and consistency regularization across diverse teacher architectures. This approach enhances model robustness, generalization, and efficiency, particularly in scenarios with limited labeled data or noisy teacher information, impacting fields ranging from medical image analysis to reinforcement learning. The resulting improvements in model performance and adaptability have significant implications for various applications.