Knowledge Transfer between Neural Networks

Knowledge transfer between neural networks involves techniques for transferring knowledge or learned features from one network to another, which can enhance performance on tasks and improve learning efficiency, especially in scenarios with limited data. This paper discusses various methodologies for knowledge transfer, including fine-tuning, distillation, and shared representations, as well as challenges and future directions in the field.