Conventional Knowledge Distillation

Conventional knowledge distillation (KD) is a model compression technique that transfers knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, improving the student's performance. Recent research focuses on refining KD methods by addressing limitations such as unequal weighting of losses, reliance on teacher logits (instead of just decisions), and the challenges of heterogeneous teacher-student architectures. These advancements, including techniques like adaptive loss weighting and novel distance metrics for feature alignment, are improving the accuracy and applicability of KD across diverse tasks, such as image recognition, natural language processing, and object detection. The resulting smaller, faster models have significant implications for deploying machine learning models on resource-constrained devices and improving efficiency in various applications.