Reading Note: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
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TITLE: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
AUTHOR: Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun
ASSOCIATION: Megvii Inc (Face++)
FROM: arXiv:1707.01083
CONTRIBUTIONS
- Two operations, pointwise group convolution and channel shuffle, are proposed to greatly reduce computation cost while maintaining accuracy.
MobileNet Architecture
In MobileNet and other works, efficient depthwise separable convolutions or group convolutions strike an excellent trade-off between representation capability and computational cost. However, both designs do not fully take the
Channel Shuffle for Group Convolutions
In order to address the mentioned issue, a straightforward solution is applying group convolutions on
ShuffleNet Unit
The following figure shows the ShuffleNet Unit.
In the figure, (a) is the building block in ResNeXt, and (b) is the building block in ShuffleNet. Given the input size
Network Architecture
Comparison
- Reading Note: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
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