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Updated: May 14, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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紧的CNN模块在功能多样性和冗余性之间进行平衡

Huihuang Zhang1, Haigen Hu1, Deming Zhou1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China; Key Laboratory of Visual Media Intelligent Processing Technology of Zhejiang Province, Hangzhou, 310023, China.

Neural networks : the official journal of the International Neural Network Society
|April 12, 2025
PubMed
概括

我们介绍BDRConv,这是一款新的卷积神经网络 (CNN) 模块,可以平衡功能多样性和冗余性. 这种紧的设计提高了模型的准确性,同时大大降低了用于改进CNN应用程序的计算成本和参数.

关键词:
紧的CNN模块是一个模块.特性多样性 特性多样性功能冗余性的特征.模型的压缩压缩.

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