Abstract
This paper proposes an iterative sequential knowledge transfer (KT) technique suitable for teacher-student framework (TSF)-based image classification when a state-of-the-art residual network (ResNet) is used for the TSF. To this end, we first extracted densely distilled knowledge in terms of the flow between the layers in the teacher ResNet. Subsequently, we iteratively and sequentially trained the student ResNet using the densely extracted flow-based teacher information. When using the proposed method, the trained student ResNet exhibited better performance in terms of classification accuracy and fast learning than the existing TSF-based KT methods considered in this study.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
