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論文筆記:ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
時間 2020-12-29
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論文鏈接:https://arxiv.org/abs/1707.01083v2 中心思想 看到了在形如Xception & ResNext等網絡存在大量1x1的卷積,雖然降低了參數量,但使得Latency增加,不夠高效 使用了point-wise group conv + channel shuffle的操作來減少1x1卷積的計算負擔 channel shuffle是本文的最大特點,加強了特徵圖每
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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.
【論文學習記錄】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
5.
【論文閱讀】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
6.
【論文學習】ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
7.
Paper Reading: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
8.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices的理解
9.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices閱讀
10.
Reading Note: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
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