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March 3, 2026
Dual-perspective filter pruning via diversity and independence collaboration
CG
Chenyang Gao
QC
Qinglong Cao
Shanghai Jiao Tong University
XY
Xiwen Yao
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Puntos clave
Filter pruning enhances neural network efficiency, leading to better resource utilization.
The method utilizes diversity and independence to optimize filter selection in networks while maintaining performance.
Analysis of neural network architectures demonstrates the effectiveness of the proposed dual-perspective approach.
Findings suggest practical applications for reducing computational load in deep learning models.
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Cite This Study
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Gao et al. (Tue,) studied this question.
synapsesocial.com/papers/69a761e2c6e9836116a2ff77
https://doi.org/https://doi.org/10.1016/j.patcog.2026.113321
Poda de filtros desde una doble perspectiva a través de la colaboración en diversidad e independencia | Synapse