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Pruning Convolutional Neural Networks For Resource Efficient Inference
時間 2020-12-23
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論文地址:https://arxiv.org/abs/1611.06440v2 英偉達出品的模型剪枝論文,NVIDIA Transfer Learning Toolkit就是基於這篇論文進行實現的? 0 摘要 我們提出了一種新的對神經網絡中卷積核進行剪枝的算法以實現高效推理。我們將基於貪婪標準的修剪與通過反向傳播的微調交錯 - 實現了一種高效的的過程,在修剪後的網絡中保持了良好的泛化能力。我們提出
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相關文章
1.
Pruning convolutional neural networks for resource efficent inference
2.
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
3.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
4.
Paper Reading:MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
5.
EffNet: An Efficient Structure for Convolutional Neural Networks
6.
【Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning】論文筆記
7.
Coarse pruning of convolutional neural networks with random masks
8.
Channel Pruning for Accelerating Very Deep Neural Networks
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論文總結:Quantizing deep convolutional networks for efficient inference: A whitepaper
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論文筆記:Quantizing deep convolutional networks for efficient inference: A whitepaper
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