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[ieee 2017 ieee international conference on robotics and automation (icra) - singapore, singapore (2017.5.29--2017.6.3)] 2017 ieee international conference on robotics and automation (icra) - deep fruit detection in orchards. 3626--3633."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITOEC.2017.8122370"},{"key":"e_1_3_2_1_10_1","volume-title":"A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282","author":"Cheng Y.","year":"2017","unstructured":"Cheng , Y. , Wang , D. , Zhou , P. , & Zhang , T ( 2017 ). A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282 . Cheng, Y., Wang, D., Zhou, P., & Zhang, T (2017). A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282."},{"key":"e_1_3_2_1_11_1","volume-title":"Data-free parameter pruning for deep neural networks. arXiv preprint arXiv:1507.06149","author":"Srinivas S.","year":"2015","unstructured":"Srinivas , S. , & Babu , R. V ( 2015 ). Data-free parameter pruning for deep neural networks. arXiv preprint arXiv:1507.06149 . Srinivas, S., & Babu, R. V (2015). Data-free parameter pruning for deep neural networks. arXiv preprint arXiv:1507.06149."},{"key":"e_1_3_2_1_12_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149","author":"Han S.","year":"2015","unstructured":"Han , S. , Mao , H. , & Dally , W. J ( 2015 ). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 . Han, S., Mao, H., & Dally, W. J (2015). 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Multi-bias non-linear activation in deep neural networks[C]\/\/International conference on machine learning. 2016: 221--229."},{"key":"e_1_3_2_1_20_1","volume-title":"Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer","author":"Komodakis N.","year":"2017","unstructured":"Komodakis , N. , & Zagoruyko , S ( 2017 ). Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer . Komodakis, N., & Zagoruyko, S (2017). Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer."},{"key":"e_1_3_2_1_21_1","volume-title":"Net2Net: Accelerating Learning via Knowledge Transfer. ICLR","author":"Chen T.","year":"2016","unstructured":"Chen , T. , Goodfellow , I. , & Shlens , J ( 2016 ). Net2Net: Accelerating Learning via Knowledge Transfer. ICLR . Chen, T., Goodfellow, I., & Shlens, J (2016). Net2Net: Accelerating Learning via Knowledge Transfer. ICLR."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_1_23_1","volume-title":"Squeezenet: alexnet-level accuracy with 50x fewer parameters and <0.5mb model size","author":"Iandola F. N.","year":"2016","unstructured":"Iandola , F. N. , Han , S. , Moskewicz , M. W. , Ashraf , K. , Dally , W. J. , & Keutzer , K ( 2016 ). Squeezenet: alexnet-level accuracy with 50x fewer parameters and <0.5mb model size . Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K (2016). Squeezenet: alexnet-level accuracy with 50x fewer parameters and <0.5mb model size."},{"key":"e_1_3_2_1_24_1","volume-title":"SqueezeNext: Hardware-Aware Neural Network Design","author":"Gholami A.","year":"2018","unstructured":"Gholami , A. , Kwon , K. , Wu , B. , Tai , Z. , & Keutzer , K ( 2018 ). SqueezeNext: Hardware-Aware Neural Network Design . 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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices[J].  Zhang X Zhou X Lin M et al. (2017). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices[J].","DOI":"10.1109\/CVPR.2018.00716"},{"key":"e_1_3_2_1_28_1","volume-title":"Shufflenet v2: practical guidelines for efficient cnn architecture design","author":"Ma N.","year":"2018","unstructured":"Ma , N. , Zhang , X. , Zheng , H. T. , & Sun , J ( 2018 ). Shufflenet v2: practical guidelines for efficient cnn architecture design . Ma, N., Zhang, X., Zheng, H. T., & Sun, J (2018). Shufflenet v2: practical guidelines for efficient cnn architecture design."},{"key":"e_1_3_2_1_29_1","volume-title":"[ieee 2016 ieee conference on computer vision and pattern recognition (cvpr) - las vegas, nv, usa","author":"Szegedy C.","year":"2016","unstructured":"Szegedy , C. , Vanhoucke , V. , Ioffe , S. , Shlens , J. , & Wojna , Z ( 2016 ). [ieee 2016 ieee conference on computer vision and pattern recognition (cvpr) - las vegas, nv, usa (2016.6.27-2016.6.30)] 2016 ieee conference on computer vision and pattern recognition (cvpr) - rethinking the inception architecture for computer vision. 2818--2826. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z (2016). [ieee 2016 ieee conference on computer vision and pattern recognition (cvpr) - las vegas, nv, usa (2016.6.27-2016.6.30)] 2016 ieee conference on computer vision and pattern recognition (cvpr) - rethinking the inception architecture for computer vision. 2818--2826."},{"key":"e_1_3_2_1_30_1","volume-title":"Densely Connected Convolutional Networks. IEEE Conference on Computer Vision & Pattern Recognition.","author":"Gao H.","year":"2017","unstructured":"Gao , H. , Zhuang , L. , Maaten , L. V. D. , & Weinberger , K. Q ( 2017 ). Densely Connected Convolutional Networks. IEEE Conference on Computer Vision & Pattern Recognition. Gao, H., Zhuang, L., Maaten, L. 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Unpublished manuscript, 40(7), 1--9","author":"Krizhevsky A.","year":"2010","unstructured":"Krizhevsky , A. , & Hinton , G ( 2010 ). Convolutional deep belief networks on cifar-10. Unpublished manuscript, 40(7), 1--9 . Krizhevsky, A., & Hinton, G (2010). Convolutional deep belief networks on cifar-10. Unpublished manuscript, 40(7), 1--9."},{"key":"e_1_3_2_1_34_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1)","author":"Srivastava N.","year":"2014","unstructured":"Srivastava , N. , Hinton , G. , Krizhevsky , A. , Sutskever , I. , & Salakhutdinov , R ( 2014 ). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1) , 1929--1958. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R (2014). Dropout: a simple way to prevent neural networks from overfitting. 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