ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

@article{Zhang2018ShuffleNetAE,
  title={ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices},
  author={Xiangyu Zhang and Xinyu Zhou and Mengxiao Lin and Jian Sun},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={6848-6856}
}
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g… CONTINUE READING
Highly Influential
This paper has highly influenced 37 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 273 citations. REVIEW CITATIONS
Tweets
This paper has been referenced on Twitter 214 times. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 247 citations

274 Citations

0100200201720182019
Citations per Year
Semantic Scholar estimates that this publication has 274 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 50 references

Inceptionv4

  • C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi
  • inception-resnet and the impact of residual…
  • 2016
Highly Influential
5 Excerpts