Abstract
Knowledge distillation is a model compression technique that transfers knowledge learned by teacher networks to student networks. Existing knowledge distillation methods greatly expand the forms of knowledge, but also make the distillation models complex and symmetric. However, few studies have explored the commonalities among these methods. In this study, we propose a concise distillation framework to unify these methods and a method to construct asymmetric knowledge distillation under the framework. Asymmetric distillation aims to enable differentiated knowledge transfers for different distillation objects. We designed a multi-stage shallow-wide branch bifurcation method to distill different knowledge representations and a grouping ensemble strategy to supervise the network to teach and learn selectively. Consequently, we conducted experiments using image classification benchmarks to verify the proposed method. Experimental results show that our implementation can achieve considerable improvements over existing methods, demonstrating the effectiveness of the method and the potential of the framework.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.