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Authors
Hu, Haolin
Zeng, Huanqiang https://orcid.org/0000-0002-2802-7745
Xie, Yi
Shi, Yifan
Zhu, Jianqing
Chen, Jing
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Funding
Funding for this research was provided by:
the Natural Science Foundation of Fujian Province (2022J01294)
the National Key R &D Program of China (2021YFE0205400)
the National Natural Science Foundation of China (61976098)
the Key Program of Natural Science Foundation of Fujian Province (2023J02022)
the Natural Science Foundation for Outstanding Young Scholars of Fujian Province (2022J06023)
the Key Science and Technology Project of Xiamen City (3502Z20231005)
the Collaborative Innovation Platform Project of Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone (2021FX03)
the High-level Talent Team Project of Quanzhou City (2023CT001)
the Key Science and Technology Project of Quanzhou City (2023GZ4)
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Article History
Received: 22 August 2023
Accepted: 21 February 2024
First Online: 28 March 2024
Declarations
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: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled ‘Global Instance Relation Distillation for Convolutional Neural Network Compression.’
