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Densely connected convolutional networks[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700--4708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_24_1","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv: 1502.03167","author":"Ioffe S","year":"2015","unstructured":"Ioffe S , Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv: 1502.03167 , 2015 . Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv: 1502.03167, 2015."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Szegedy C Vanhoucke V Ioffe S etal Rethinking the inception architecture for computer vision[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2818--2826.  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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]\/\/Proceedings of the IEEE international conference on computer vision. 2015: 1026--1034.","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_2_1_28_1","volume-title":"Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980","author":"Kingma D P","year":"2014","unstructured":"Kingma D P , Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980 , 2014 . Kingma D P, Ba J. 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