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Multimedia, vol.20, no.6, pp.1576-1590, June 2018. 10.1109\/tmm.2017.2766843","DOI":"10.1109\/TMM.2017.2766843"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] Y. He, X. Zhang, and J. Sun, \u201cChannel pruning for accelerating very deep neural networks,\u201d Proc. Int. Conf. Comput. Vis., pp.1398-1406, 2017. 10.1109\/iccv.2017.155","DOI":"10.1109\/ICCV.2017.155"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang, \u201cLearning efficient convolutional networks through network slimming,\u201d Proc. Int. Conf. Comput. Vis., pp.2755-2763, 2017. 10.1109\/iccv.2017.298","DOI":"10.1109\/ICCV.2017.298"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] X. Zhang, J. Zou, K. He, and J. Sun, \u201cAccelerating very deep convolutional networks for classification and detection,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.38, no.10, pp.1943-1955, 2016. 10.1109\/tpami.2015.2502579","DOI":"10.1109\/TPAMI.2015.2502579"},{"key":"10","unstructured":"[10] S. Han, J. Pool, J. Tran, and W. Dally, \u201cLearning both weights and connections for efficient neural network,\u201d Advances in Neural Information Processing Systems, pp.1135-1143, 2015."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang, \u201cLearning efficient convolutional networks through network slimming,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp.2755-2763, 2017. 10.1109\/iccv.2017.298","DOI":"10.1109\/ICCV.2017.298"},{"key":"12","unstructured":"[12] J. Xue, J. Li, and Y. Gong, \u201cRestructuring of deep neural network acoustic models with singular value decomposition,\u201d Interspeech, pp.2365-2369, Aug. 2013."},{"key":"13","unstructured":"[13] E.L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, \u201cExploiting linear structure within convolutional networks for efficient evaluation,\u201d Proc. Adv. Neural Inf. Process. Syst., pp.1269-1277, 2016."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, \u201cGoing deeper with convolutions,\u201d Proc. Comput. vis. pattern recognit., pp.1-9, June 2015. 10.1109\/cvpr.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, \u201cRethinking the inception architecture for computer vision,\u201d Proc. Computer vision and pattern recognition (CVPR), pp.2818-2826, 2016. 10.1109\/cvpr.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] C. Szegedy, S. Ioffe, V. Vanhoucke, and A.A. 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