New hierarchical method efficiently prunes CNN filters

Researchers have developed a novel two-level hierarchical approach for whole-network filter pruning in Convolutional Neural Networks (CNNs). This method efficiently reduces model size and computational requirements by pruning filters across all layers. The approach utilizes a sparse-approximation formulation and a novel closed-form error criterion for backward pruning, outperforming existing state-of-the-art methods on various benchmark networks like ResNet and VGG. AI

IMPACT Reduces model size and computational demands for CNNs, enabling deployment on resource-constrained devices.

RANK_REASON The cluster contains a research paper detailing a new method for optimizing neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

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• CIFAR-10
• ResNet18
• ResNet32
• ResNet56
• ResNext101
• Sourangshu Bhattacharya
• VGG16

• paper
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