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Fei-Fei, \u201cImagenet large scale visual recognition challenge,\u201d International Journal of Computer Vision (IJCV), vol.115, no.3, pp.211-252, 2015. 10.1007\/s11263-015-0816-y","DOI":"10.1007\/s11263-015-0816-y"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, \u201cImageNet: a Large-Scale Hierarchical Image Database,\u201d 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.248-255, 2009. 10.1109\/cvpr.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] A. Krizhevsky, I. Sutskever, and G.E. 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Adam, \u201cMobilenets: Efficient convolutional neural networks for mobile vision applications,\u201d arXiv preprint: 1704.04861, 2017."},{"key":"11","unstructured":"[11] H. Gao, Z. Wang, and S. Ji, \u201cChannelNets: Compact and efficient convolutional neural networks via channel-wise convolutions,\u201d Conference and Workshop on Neural Information Processing Systems (NIPS), 2018."},{"key":"12","unstructured":"[12] C.Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, \u201cDeeply-supervised nets,\u201d AISTATS, 2015."},{"key":"13","unstructured":"[13] A. Krizhevsky, \u201cLearning multiple layers of features from tiny images,\u201d Technical Report, University of Toronto, 2009."},{"key":"14","unstructured":"[14] I. Sutskever, J. Martens, G. Dahl, and G. Hinton, \u201cOn the importance of initialization and momentum in deep learning,\u201d International Conference on Machine Learning (ICML), 2013."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] G. Huang, Z. 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