{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:19:11Z","timestamp":1767183551565,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"S4","license":[{"start":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T00:00:00Z","timestamp":1519948800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"The National Science and Technology Major Project","award":["2017YFB0803001"],"award-info":[{"award-number":["2017YFB0803001"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61502048"],"award-info":[{"award-number":["61502048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The National \u201c242\u201d Information Security Program","award":["2015A136"],"award-info":[{"award-number":["2015A136"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s10586-018-2165-4","type":"journal-article","created":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T08:52:28Z","timestamp":1519980748000},"page":"9371-9383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Weighted pooling for image recognition of deep convolutional neural networks"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8369-8445","authenticated-orcid":false,"given":"Xiaoning","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyue","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bojian","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lize","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,3,2]]},"reference":[{"key":"2165_CR1","unstructured":"Zeiler, M. D., Fergus, R.: Stochastic pooling for regularization of deep convolutional neural networks. Eprint Arxiv (2013)"},{"issue":"4","key":"2165_CR2","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(4), 379\u2013423 (1948)","journal-title":"Bell Syst. Tech. J."},{"key":"2165_CR3","unstructured":"Krizhevsky, A.: Learning multiple layers of featurs from tiny images. Technical Report TR-2009, University of Toronto (2009)"},{"key":"2165_CR4","unstructured":"LeCun, Y.: The MNIST database. \nhttp:\/\/yann.lecun.com\/exdb\/mnist\/\n\n (2012)"},{"key":"2165_CR5","unstructured":"Ba, J. L., Kiros, J. R., Hinton, G. E. Layer normalization (2016)"},{"issue":"4","key":"2165_CR6","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jacke, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"key":"2165_CR7","unstructured":"LeCun, Y., Boser, B., Denker, J. S., Howard, R. E., Habbard, W., Jackel, L. D., Henderson, D.: Handwritten digit recognition with a back-propagation network. In: Proceedings of Advances in Neural Information Processing Systems 2, pp. 396\u2013404. Morgan Kaufmann Publishers Inc., San Francisco (1990)"},{"key":"2165_CR8","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G. E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106\u20131114 (2012)"},{"key":"2165_CR9","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014 )"},{"key":"2165_CR10","unstructured":"Simonyan, K. Zisserman, A.: Two-stream convolutional networks for action recognition in videos. CoRR, abs\/1406.2199, 2014. Published in Proceeding NIPS (2014)"},{"key":"2165_CR11","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y. et al.: Going deeper with convolutions. pp. 1\u20139 (2014)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2165_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition. pp. 770\u2013778, IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2165_CR13","first-page":"1","volume":"99","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for scene segmentation. IEEE Trans. Pattern Anal. 99, 1 (2017)","journal-title":"IEEE Trans. Pattern Anal."},{"issue":"4","key":"2165_CR14","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1109\/TSMC.2016.2629509","volume":"47","author":"B Zhang","year":"2017","unstructured":"Zhang, B., Li, Z., Cao, X., Ye, Q., Chen, C., Shen, L., Perina, A., Ji, R.: Output constraint transfer for kernelized correlation filter in tracking. IEEE Trans. Syst. Man Cybernet. 47(4), 693\u2013703 (2017)","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"2165_CR15","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, B., Yang, W.. Boosting-like deep convolutional network for pedestrian detection. In: Biometric Recognition. Springer International Publishing (2015)","DOI":"10.1007\/978-3-319-25417-3_68"},{"key":"2165_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, B., Gu, J., Chen, C., Han, J., Su, X., Cao, X., Liu, J.: One-two-one network for compression artifacts reduction in remote sensing, In: ISPRS Journal of Photogrammetry and Remote Sensing (2018)","DOI":"10.1016\/j.isprsjprs.2018.01.003"},{"issue":"3","key":"2165_CR17","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1016\/j.automatica.2013.12.035","volume":"50","author":"B Zhang","year":"2014","unstructured":"Zhang, B., Liu, W., Mao, Z., et al.: Cooperative and geometric learning algorithm (CGLA) for path planning of UAVs with limited information. Automatica 50(3), 809\u2013820 (2014)","journal-title":"Automatica"},{"issue":"3","key":"2165_CR18","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2014","unstructured":"Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2014)","journal-title":"Int. J. Comput. Vis."},{"key":"2165_CR19","unstructured":"Abadi, M., Agarwal, A., Barham, P. et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems (2016)"},{"key":"2165_CR20","unstructured":"Kingma, D. P., Adam, J. B: A method for stochastic optimization. Comput. Sci. (2014)"},{"key":"2165_CR21","doi-asserted-by":"crossref","unstructured":"Zeiler, M. D., Fergus, R.: Visualizing and understanding convolutional networks. 8689, pp. 818\u2013833 (2014)","DOI":"10.1007\/978-3-319-10590-1_53"},{"issue":"2","key":"2165_CR22","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u201366 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"2165_CR23","first-page":"249","volume":"9","author":"X Glorot","year":"2010","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249\u2013256 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"2165_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang ,X., Ren, S. et al.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"2165_CR25","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on International Conference on Machine Learning. JMLR.org, pp. 448\u2013456 (2015)"},{"issue":"7","key":"2165_CR26","first-page":"257","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(7), 257\u2013269 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"2165_CR27","unstructured":"Zeiler, M. D.: ADADELTA: an adaptive learning rate method. In: Computer Science (2012)"},{"key":"2165_CR28","unstructured":"Boureau, Y. L., Ponce, J., Lecun, Y.: A theoretical analysis of feature pooling in visual recognition. In: International Conference on Machine Learning. DBLP, pp. 111\u2013118 (2010)"},{"issue":"1","key":"2165_CR29","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","volume":"160","author":"DH Hubel","year":"1962","unstructured":"Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex. J. Physiol. 160(1), 106 (1962)","journal-title":"J. Physiol."},{"issue":"2\u20133","key":"2165_CR30","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1023\/A:1008065931878","volume":"31","author":"JJ Koenderink","year":"1999","unstructured":"Koenderink, J.J., Van Doorn, A.J.: The structure of locally orderless images. Int. J. Comput. Vis. 31(2\u20133), 159\u2013168 (1999)","journal-title":"Int. J. Comput. Vis."},{"key":"2165_CR31","unstructured":"Graham, B.: Fractional max-pooling. Eprint Arxiv (2014)"},{"key":"2165_CR32","doi-asserted-by":"crossref","unstructured":"Harada, T., Ushiku, Y., Yamashita, Y. et al.: Discriminative spatial pyramid. In: Computer Vision and Pattern Recognition. IEEE, pp. 1617\u20131624 (2011)","DOI":"10.1109\/CVPR.2011.5995691"},{"issue":"9","key":"2165_CR33","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"2165_CR34","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/S0375-9601(00)00725-8","volume":"277","author":"EG Fan","year":"2000","unstructured":"Fan, E.G.: Extended tanh-function method and its applications to nonlinear equations. Phys. Lett.s A 277(4), 212\u2013218 (2000)","journal-title":"Phys. Lett.s A"},{"issue":"4","key":"2165_CR35","first-page":"212","volume":"3","author":"GE Hinton","year":"2012","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., et al.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212\u2013223 (2012)","journal-title":"Comput. Sci."},{"key":"2165_CR36","unstructured":"Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge, VOC 2007 Results (2007)"},{"issue":"6","key":"2165_CR37","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., Girshick, R., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-018-2165-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10586-018-2165-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-018-2165-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T13:23:50Z","timestamp":1575293030000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10586-018-2165-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,2]]},"references-count":37,"journal-issue":{"issue":"S4","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["2165"],"URL":"https:\/\/doi.org\/10.1007\/s10586-018-2165-4","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2018,3,2]]},"assertion":[{"value":"1 December 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}