Multi Teacher Knowledge Distillation

Multi-teacher knowledge distillation improves the training of smaller, more efficient "student" neural networks by leveraging the knowledge of multiple larger, more powerful "teacher" networks. Current research focuses on optimizing the selection and weighting of teachers, developing adaptive strategies for dynamically adjusting teacher influence based on data sample characteristics, and designing novel loss functions to enhance knowledge transfer across diverse modalities (e.g., text, images, audio). This technique is proving valuable across various applications, including image processing, natural language processing, and even mental health detection, by enabling the deployment of high-performing models on resource-constrained devices or datasets.