Teacher Network

Teacher networks are a core component of knowledge distillation, a technique for transferring knowledge from a large, complex model (the teacher) to a smaller, more efficient model (the student). Current research focuses on improving knowledge transfer efficiency and robustness, particularly in imbalanced datasets and across diverse student architectures, often employing teacher-student networks with various loss functions and regularization techniques. This research is significant because it enables the deployment of high-performing deep learning models on resource-constrained devices and improves the generalization capabilities of smaller models, impacting fields like computer vision, natural language processing, and medical image analysis.