Distillation Method

Knowledge distillation is a machine learning technique that transfers knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, aiming to improve the student's performance and reduce computational costs. Current research focuses on improving distillation across diverse architectures (CNNs, Transformers, diffusion models) by addressing challenges like performance gaps between teacher and student, handling few-class problems, and optimizing for specific applications such as document understanding and text-to-audio generation. These advancements are significant because they enable the deployment of powerful models in resource-constrained environments and improve the efficiency of training and inference, impacting various fields from computer vision to natural language processing.