Distillation Framework

Knowledge distillation frameworks aim to transfer 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 applying distillation to diverse model architectures, including diffusion models for image generation and super-resolution, large language models for text generation and reasoning, and neural networks for various computer vision tasks. This technique is significant because it enables deployment of high-performing models on resource-constrained devices and improves the efficiency of training complex models, impacting fields ranging from image processing and natural language processing to robotics and personalized medicine.