{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T18:26:49Z","timestamp":1772562409361,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,7,15]],"date-time":"2019-07-15T00:00:00Z","timestamp":1563148800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,7,15]],"date-time":"2019-07-15T00:00:00Z","timestamp":1563148800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61822402"],"award-info":[{"award-number":["61822402"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61574044"],"award-info":[{"award-number":["61574044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61574046"],"award-info":[{"award-number":["61574046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61674042"],"award-info":[{"award-number":["61674042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s00521-019-04316-4","type":"journal-article","created":{"date-parts":[[2019,7,15]],"date-time":"2019-07-15T04:02:42Z","timestamp":1563163362000},"page":"8507-8516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Nonlinear CNN: improving CNNs with quadratic convolutions"],"prefix":"10.1007","volume":"32","author":[{"given":"Yiyang","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Hengliang","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Dian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,15]]},"reference":[{"key":"4316_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M et al (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint \narXiv:1603.04467"},{"key":"4316_CR2","first-page":"821","volume":"25","author":"MA Aizerman","year":"1964","unstructured":"Aizerman MA (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 25:821\u2013837","journal-title":"Autom Remote Control"},{"issue":"10","key":"4316_CR3","doi-asserted-by":"publisher","first-page":"2678","DOI":"10.1162\/NECO_a_00018","volume":"22","author":"Y Cho","year":"2010","unstructured":"Cho Y, Saul LK (2010) Large-margin classification in infinite neural networks. Neural Comput 22(10):2678\u20132697","journal-title":"Neural Comput"},{"key":"4316_CR4","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR, 2009. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"4316_CR5","unstructured":"Everingham M, Van\u00a0Gool L, Williams CKI, Winn J, Zisserman A (2007) The PASCAL visual object classes challenge (VOC2007) results. \nhttp:\/\/www.pascal-network.org\/challenges\/VOC\/voc2007\/workshop\/index.html\n\n. Accessed 1 Dec 2017"},{"key":"4316_CR6","unstructured":"Everingham M, Van\u00a0Gool L, Williams CKI, Winn J, Zisserman A (2012) The PASCAL visual object classes challenge (VOC2012) results. \nhttp:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html\n\n. Accessed 1 Dec 2017"},{"key":"4316_CR7","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. arXiv preprint \narXiv:1504.08083","DOI":"10.1109\/ICCV.2015.169"},{"key":"4316_CR8","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"4316_CR9","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, pp 249\u2013256"},{"key":"4316_CR10","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"4316_CR11","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press, Cambridge"},{"key":"4316_CR12","doi-asserted-by":"crossref","unstructured":"Horn RA (1990) The Hadamard product. In: Proceedings of symposia in applied mathematics, vol 40, pp 87\u2013169","DOI":"10.1090\/psapm\/040\/1059485"},{"key":"4316_CR13","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448\u2013456"},{"key":"4316_CR14","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neucom.2018.09.061","volume":"322","author":"Y Ji","year":"2018","unstructured":"Ji Y, Zhang H, Wu QMJ (2018) Salient object detection via multi-scale attention CNN. Neurocomputing 322:130\u2013140","journal-title":"Neurocomputing"},{"key":"4316_CR15","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia, pp 675\u2013678","DOI":"10.1145\/2647868.2654889"},{"key":"4316_CR16","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint \narXiv:1412.6980"},{"key":"4316_CR17","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"4","key":"4316_CR18","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 JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551","journal-title":"Neural Comput"},{"issue":"6","key":"4316_CR19","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0893-6080(05)80131-5","volume":"6","author":"M Leshno","year":"1993","unstructured":"Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw 6(6):861\u2013867","journal-title":"Neural Netw"},{"key":"4316_CR20","unstructured":"Lin T-Y, RoyChowdhury A, Maji S (2015) Bilinear CNN models for fine-grained visual recognition. In: Proceedings of the IEEE international conference on computer vision, pp 1449\u20131457"},{"key":"4316_CR21","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"4316_CR22","unstructured":"Mairal J (2016) End-to-end kernel learning with supervised convolutional kernel networks. Advances in neural information processing systems pp 1399\u20131407"},{"key":"4316_CR23","unstructured":"Mairal J, Koniusz P, Harchaoui Z, Schmid C (2014) Convolutional kernel networks. Advances in neural information processing systems, pp 2627\u20132635"},{"key":"4316_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3333-9","author":"\u015e \u00d6zt\u00fcrk","year":"2017","unstructured":"\u00d6zt\u00fcrk \u015e, Akdemir B (2017) A convolutional neural network model for semantic segmentation of mitotic events in microscopy images. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-017-3333-9","journal-title":"Neural Comput Appl"},{"key":"4316_CR25","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.procs.2018.05.166","volume":"132","author":"\u015e \u00d6zt\u00fcrk","year":"2018","unstructured":"\u00d6zt\u00fcrk \u015e, Akdemir B (2018) Effects of histopathological image pre-processing on convolutional neural networks. Procedia Comput Sci 132:396\u2013403","journal-title":"Procedia Comput Sci"},{"key":"4316_CR26","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271 \u00a0","DOI":"10.1109\/CVPR.2017.690"},{"key":"4316_CR27","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"issue":"6","key":"4316_CR28","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"4316_CR29","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Hao S, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis (IJCV) 115(3):211\u2013252. \nhttps:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis (IJCV)"},{"key":"4316_CR30","unstructured":"Saxe AM, McClelland JL, Ganguli S (2013) Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint \narXiv:1312.6120"},{"key":"4316_CR31","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint \narXiv:1409.1556"},{"key":"4316_CR32","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Training very deep networks. In: Advances in neural information processing systems, pp 2377\u20132385"},{"key":"4316_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A et al (2015) Going deeper with convolutions. In: CVPR","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"4316_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"4316_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3579-x","author":"H Zhang","year":"2018","unstructured":"Zhang H, Ji Y, Huang W, Liu L (2018) Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-018-3579-x","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04316-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04316-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04316-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T23:17:04Z","timestamp":1594682224000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04316-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,15]]},"references-count":35,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["4316"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04316-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,15]]},"assertion":[{"value":"24 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}