{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T23:36:44Z","timestamp":1778974604545,"version":"3.51.4"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSF of China","doi-asserted-by":"publisher","award":["61973162"],"award-info":[{"award-number":["61973162"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"NSF of Jiangsu Province","doi-asserted-by":"publisher","award":["BZ2021013"],"award-info":[{"award-number":["BZ2021013"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"ARC","doi-asserted-by":"publisher","award":["DP180103424"],"award-info":[{"award-number":["DP180103424"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"ARC","doi-asserted-by":"publisher","award":["DE190101473"],"award-info":[{"award-number":["DE190101473"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1109\/tnnls.2021.3131377","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T20:33:22Z","timestamp":1639168402000},"page":"5828-5840","source":"Crossref","is-referenced-by-count":13,"title":["Understanding How Pretraining Regularizes Deep Learning Algorithms"],"prefix":"10.1109","volume":"34","author":[{"given":"Yu","family":"Yao","sequence":"first","affiliation":[{"name":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baosheng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-4092-9856","authenticated-orcid":false,"given":"Chen","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-9640-6472","authenticated-orcid":false,"given":"Tongliang","family":"Liu","sequence":"additional","affiliation":[{"name":"PCA Laboratory, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2692773"},{"key":"ref57","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":"ref12","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref56","first-page":"6835","article-title":"Are anchor points really indispensable in label-noise learning?","author":"xia","year":"2019","journal-title":"Proc NIPS"},{"key":"ref15","article-title":"Auto-encoding variational Bayes","author":"p kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01157"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"ref52","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"ref55","first-page":"8527","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","author":"han","year":"2018","journal-title":"Proc NIPS"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref16","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","author":"coates","year":"2011","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2712793"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00305"},{"key":"ref51","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"ArXiv 1708 07747"},{"key":"ref50","author":"mohri","year":"2018","journal-title":"Foundations of Machine Learning"},{"key":"ref46","first-page":"207","article-title":"Deep Gaussian processes","author":"damianou","year":"2013","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178573"},{"key":"ref48","first-page":"2159","article-title":"Algorithmic stability and hypothesis complexity","author":"liu","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","article-title":"A probabilistic theory of deep learning","author":"patel","year":"2015","journal-title":"arXiv 1504 00641"},{"key":"ref42","first-page":"1609","article-title":"A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models","volume":"13","author":"lawrence","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1530509100"},{"key":"ref44","article-title":"Graph signal processing—A probabilistic framework","author":"zhang","year":"2015"},{"key":"ref43","author":"whittaker","year":"2009","journal-title":"Graphical Models in Applied Multivariate Statistics"},{"key":"ref49","first-page":"499","article-title":"Stability and generalization","volume":"2","author":"bousquet","year":"2002","journal-title":"J Mach Learn Res"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2919705"},{"key":"ref7","article-title":"What do neural networks learn when trained with random labels?","author":"maennel","year":"2020","journal-title":"arXiv 2006 10455"},{"key":"ref9","article-title":"Deep residual learning for image recognition","author":"he","year":"2015","journal-title":"arXiv 1512 03385"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2016.04.003"},{"key":"ref3","article-title":"On the approximation properties of neural networks","author":"siegel","year":"2019","journal-title":"arXiv 1904 02311"},{"key":"ref6","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":"ref5","article-title":"Deep neural network approximation theory","author":"elbr\u00e4chter","year":"2019","journal-title":"arXiv 1901 02220"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2740318"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2008.11-07-647"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273556"},{"key":"ref37","author":"zhu","year":"2003","journal-title":"Semi-Supervised Learning From Gaussian Fields to Gaussian Processes"},{"key":"ref36","first-page":"1345","article-title":"Modeling human motion using binary latent variables","author":"taylor","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","first-page":"1","article-title":"An analysis of unsupervised pre-training in light of recent advances","author":"paine","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref30","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":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2919948"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","article-title":"Neural network with unbounded activation functions is universal approximator","author":"sonoda","year":"2015","journal-title":"arXiv 1505 03654"},{"key":"ref1","first-page":"625","article-title":"Why does unsupervised pre-training help deep learning?","volume":"11","author":"erhan","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2935027"},{"key":"ref38","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"belkin","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"ref26","first-page":"1","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2868847"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2966746"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2551940"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2019-1873"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00165"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref27","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2869694"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00814"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2308519"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.108973"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/xplorestaging.ieee.org\/ielx7\/5962385\/10237282\/09646441.pdf?arnumber=9646441","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:28:23Z","timestamp":1695666503000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/document\/9646441\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":64,"journal-issue":{"issue":"9"},"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/tnnls.2021.3131377","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}