A Comprehensive Review of Knowledge Distillation in Computer Vision
It addresses the problem of model size and complexity for practitioners in computer vision, but it is incremental as it is a review paper.
This review paper examines knowledge distillation as a technique to compress complex deep learning models for deployment in resource-constrained environments, summarizing its principles, techniques, and applications in computer vision.
Deep learning techniques have been demonstrated to surpass preceding cutting-edge machine learning techniques in recent years, with computer vision being one of the most prominent examples. However, deep learning models suffer from significant drawbacks when deployed in resource-constrained environments due to their large model size and high complexity. Knowledge Distillation is one of the prominent solutions to overcome this challenge. This review paper examines the current state of research on knowledge distillation, a technique for compressing complex models into smaller and simpler ones. The paper provides an overview of the major principles and techniques associated with knowledge distillation and reviews the applications of knowledge distillation in the domain of computer vision. The review focuses on the benefits of knowledge distillation, as well as the problems that must be overcome to improve its effectiveness.