{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:37:14Z","timestamp":1772908634772,"version":"3.50.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China 973 Program","doi-asserted-by":"crossref","award":["2015CB351705"],"award-info":[{"award-number":["2015CB351705"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61332018"],"award-info":[{"award-number":["61332018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2017]]},"DOI":"10.1109\/tnnls.2017.2705682","type":"journal-article","created":{"date-parts":[[2017,6,9]],"date-time":"2017-06-09T18:29:20Z","timestamp":1497032960000},"page":"1-14","source":"Crossref","is-referenced-by-count":12,"title":["Improving CNN Performance Accuracies With Min-Max Objective"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8820-8970","authenticated-orcid":false,"given":"Weiwei","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2162429"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2014.6889447"},{"key":"ref33","first-page":"2094","article-title":"Discriminative transfer learning with tree-based priors","author":"srivastava","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","first-page":"2951","article-title":"Practical Bayesian optimization of machine learning algorithms","author":"snoek","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","article-title":"Improving deep neural networks with probabilistic Maxout units","author":"springenberg","year":"2014","journal-title":"Proc ICLR"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2012.01.010"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143958"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref35","author":"lee","year":"2014","journal-title":"Deeplysupervised Nets"},{"key":"ref34","author":"hinton","year":"2012","journal-title":"Improving Neural Networks by Preventing Co-adaptation of Feature Detectors"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0700622104"},{"key":"ref27","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref29","article-title":"Human versus machine: Comparing visual object recognition systems on a level playing field","author":"pinto","year":"2010","journal-title":"Proc Comput Syst Neurosci"},{"key":"ref2","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"},{"key":"ref1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","author":"malinowski","year":"2013","journal-title":"Learnable Pooling Regions for Image Classification"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref24","first-page":"1058","article-title":"Regularization of neural networks using dropconnect","author":"wan","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref23","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2134090"},{"key":"ref25","first-page":"950","article-title":"A simple weight decay can improve generalization","volume":"4","author":"krogh","year":"1995","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-011-9272-x"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.250598"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1936.tb02137.x"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2608001"},{"key":"ref10","first-page":"1988","article-title":"Deep learning face representation by joint identification-verification","author":"sun","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","first-page":"809","article-title":"Learning a deep compact image representation for visual tracking","author":"wang","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654948"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.224"},{"key":"ref15","article-title":"Network in network","author":"lin","year":"2014","journal-title":"Proc ICLR"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref17","first-page":"1319","article-title":"Maxout networks","author":"goodfellow","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref19","article-title":"Stochastic pooling for regularization of deep convolutional neural networks","author":"zeiler","year":"2013","journal-title":"Proc ICLR"},{"key":"ref4","author":"he","year":"2015","journal-title":"Deep residual learning for image recognition"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref5","author":"szegedy","year":"2014","journal-title":"Scalable high-quality object detection"},{"key":"ref8","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref49","article-title":"Labeled faces in the wild: A database for studying face recognition in unconstrained environments","author":"huang","year":"2007"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.242"},{"key":"ref46","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref45","article-title":"Multi-digit number recognition from street view imagery using deep convolutional neural networks","author":"goodfellow","year":"2013","journal-title":"Proc ICLR"},{"key":"ref48","author":"yi","year":"2014","journal-title":"Learning face representation from scratch"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.180"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref41","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"ref43","first-page":"5","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/6104215\/07945277.pdf?arnumber=7945277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:25:49Z","timestamp":1642004749000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7945277\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/tnnls.2017.2705682","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}