Knowledge Distillation - Methods and Implementations: Studying knowledge distillation methods for transferring knowledge from large, complex models to smaller, more efficient ones

Abstract

Knowledge distillation is a technique used to transfer knowledge from a large, complex model (teacher) to a smaller, more efficient one (student). This paper provides a comprehensive overview of knowledge distillation methods and implementations. We first discuss the motivation behind knowledge distillation and its applications. Next, we review the key concepts and components of knowledge distillation, including the teacher-student architecture, loss functions, and training strategies. We then delve into various knowledge distillation methods, such as traditional knowledge distillation, attention-based distillation, and self-distillation. We also explore different implementations of knowledge distillation, including distillation for image classification, object detection, and natural language processing tasks. Finally, we discuss challenges and future directions in knowledge distillation research.

Knowledge distillation is a technique used to transfer knowledge from a large, complex model (teacher) to a smaller, more efficient one (student). This paper provides a comprehensive overview of knowledge distillation methods and implementations. We first discuss the motivation behind knowledge distillation and its applications. Next, we review the key concepts and components of knowledge distillation, including the teacher-student architecture, loss functions, and training strategies. We then delve into various knowledge distillation methods, such as traditional knowledge distillation, attention-based distillation, and self-distillation. We also explore different implementations of knowledge distillation, including distillation for image classification, object detection, and natural language processing tasks. Finally, we discuss challenges and future directions in knowledge distillation research.