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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2020: Intelligent Information and Database Systems pp 163–173Cite as

Slice Operator for Efficient Convolutional Neural Network Architecture

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12034))

Abstract

Convolutional neural networks (CNNs) have shown remarkable performance in various computer vision tasks in recent years. However, the increasing model size has raised challenges in adopting them in real-time applications as well as mobile and embedded vision applications. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, MobileNetV2, and ShuffleNetV2. This paper describes an improved version of ShuffleNetV2, which uses the Channel Slice operator with slice-step parameters to make information interaction between two channels, instead of using Channel Shuffle and Channel Split operators. Because the Channel Slice and Channel Split operators are similar and the proposed architecture does not have Channel Shuffle operator, it has lower memory access cost than ShuffleNetV2. The experiments on ImageNet demonstrate that the proposed network is faster than ShuffleNetV2 while still achieves similar accuracy.

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Notes

  1. 1.

    https://gluon-cv.mxnet.io/.

References

  1. Chen, T., et al.: Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. In: Proceedings of the Neural Information Processing Systems, Workshop on Machine Learning Systems (2015)

    Google Scholar 

  2. Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J.: cuDNN: efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014)

  3. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  4. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)

    Google Scholar 

  5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  7. Hoang, V.T., Jo, K.H.: Pydmobilenet: improved version of mobilenets with pyramid depthwise separable convolution. arXiv preprint arXiv:1811.07083 (2018)

  8. Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  9. Huang, G., Liu, S., van der Maaten, L., Weinberger, K.Q.: Condensenet: an efficient densenet using learned group convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2752–2761 (2018)

    Google Scholar 

  10. Huang, G., Liu, Z., Maaten, L.V.D., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  11. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  12. Lin, M., Chen, Q., Yan, S.: Network in network. In: Proceedings of the International Conference on Learning Representations (2014)

    Google Scholar 

  13. Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8

    Chapter  Google Scholar 

  14. Nesterov, Y.E.: A method for solving the convex programming problem with convergence rate o (\(1/\text{k}^ 2\)). In: Dokl. Akad. Nauk SSSR, vol. 269, pp. 543–547 (1983)

    Google Scholar 

  15. Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22, 400–407 (1951)

    Article  MathSciNet  Google Scholar 

  16. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  17. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  18. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  19. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  20. Wang, R.J., Li, X., Ling, C.X.: Pelee: A real-time object detection system on mobile devices. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1967–1976 (2018)

    Google Scholar 

  21. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  22. Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Proceedings of the British Machine Vision Conference (2016)

    Google Scholar 

  23. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. In: Proceedings of the International Conference on Learning Representations (2018)

    Google Scholar 

  24. Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)

    Google Scholar 

  25. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018)

    Google Scholar 

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP, Ministry of Science, ICT & Future Planning) (No. 2019R1F1A1061659).

Author information

Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Ulsan, Korea

    Van-Thanh Hoang & Kang-Hyun Jo

Authors
  1. Van-Thanh Hoang
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  2. Kang-Hyun Jo
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Corresponding author

Correspondence to Kang-Hyun Jo .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

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Hoang, VT., Jo, KH. (2020). Slice Operator for Efficient Convolutional Neural Network Architecture. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-42058-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42057-4

  • Online ISBN: 978-3-030-42058-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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