基于增强PSO算法的神经架构搜索方法在工业图像分类中的应用研究发表在information sciences
Neural architecture search using an enhanced particle swarm optimization algorithm for industrial image classification
Highlights
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An enhanced PSO algorithm incorporating dynamic ring neighbourhood topology and swarm entropy mutation mechanisms is proposed.
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We develop a two-level binary particle encoding scheme to improve search efficiency.
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We use MBConv modules as basic building blocks and replace their original SE attention with CBAM mechanisms.
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Addressing the computational bottleneck in NAS, we propose a low-fidelity evaluation strategy.
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