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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
時間 2020-12-29
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depthwise separable convolution:depthwise convolution+ pointwise convolution。 depthwise convolution:比如輸入是AXA大小,M通道,輸出大小是BXB,N通道。比如卷積核大小爲KXK。depthwise convolution就是:使用M個KXK大小的卷積核,分別對輸入進行卷積,與常規的卷積不同的是,沒
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相關文章
1.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
2.
【論文閱讀筆記】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
3.
論文閱讀筆記:ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
4.
【論文閱讀】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
5.
Paper Reading: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
6.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices的理解
7.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices閱讀
8.
論文筆記:ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
9.
【論文學習記錄】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
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Reading Note: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
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