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Pruning Convolutional Neural Networks For Resource Efficient Inference
时间 2020-12-23
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论文地址:https://arxiv.org/abs/1611.06440v2 英伟达出品的模型剪枝论文,NVIDIA Transfer Learning Toolkit就是基于这篇论文进行实现的? 0 摘要 我们提出了一种新的对神经网络中卷积核进行剪枝的算法以实现高效推理。我们将基于贪婪标准的修剪与通过反向传播的微调交错 - 实现了一种高效的的过程,在修剪后的网络中保持了良好的泛化能力。我们提出
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相关文章
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
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EffNet: An Efficient Structure for Convolutional Neural Networks
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【Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning】论文笔记
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Coarse pruning of convolutional neural networks with random masks
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Channel Pruning for Accelerating Very Deep Neural Networks
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论文总结:Quantizing deep convolutional networks for efficient inference: A whitepaper
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论文笔记:Quantizing deep convolutional networks for efficient inference: A whitepaper
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