Distilling Knowledge

Knowledge distillation is a machine learning technique that transfers knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, improving the student's performance and reducing computational costs. Current research focuses on refining distillation methods to mitigate issues like teacher errors and knowledge redundancy, often employing techniques like adaptive loss functions, mixture-of-experts architectures, and active learning to optimize the knowledge transfer process. This approach is significant for improving the efficiency and accessibility of various applications, including natural language processing, computer vision, and medical image analysis, by enabling the deployment of high-performing models on resource-constrained devices.