{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:18:15Z","timestamp":1783437495113,"version":"3.54.6"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2995367","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T22:10:13Z","timestamp":1589839813000},"page":"1-1","source":"Crossref","is-referenced-by-count":21,"title":["AutoSegNet: An Automated Neural Network for Image Segmentation"],"prefix":"10.1109","author":[{"given":"Zhimin","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edmund Y.","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoungho","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ni","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Improving generalization performance by switching from Adam to SGD","author":"shirish keskar","year":"2017","journal-title":"arXiv 1712 07628"},{"key":"ref38","first-page":"4148","article-title":"The marginal value of adaptive gradient methods in machine learning","author":"wilson","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.1000502"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref37","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"loshchilov","year":"2016","journal-title":"arXiv 1608 03983"},{"key":"ref36","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref35","first-page":"2847","article-title":"On the expressive power of deep neural networks","volume":"70","author":"raghu","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3389\/fnana.2015.00142"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref12","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00017"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3115\/1075812.1075835"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330648"},{"key":"ref17","first-page":"528","article-title":"Fast Bayesian optimization of machine learning hyperparameters on large datasets","volume":"54","author":"klein","year":"2017","journal-title":"Proc Int Conf Artif Intell Statist (AISTATS)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref19","first-page":"282","article-title":"Conditional random fields: Probabilistic models for segmenting and labeling sequence data","author":"lafferty","year":"2001","journal-title":"Proc 18th Int Conf Mach Learn"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref4","first-page":"678","article-title":"Path-level network transformation for efficient architecture search","author":"cai","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32248-9_25"},{"key":"ref3","first-page":"4095","article-title":"Efficient neural architecture search via parameters sharing","volume":"80","author":"pham","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Lect Notes Comput Sci"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref5","article-title":"Designing neural network architectures using reinforcement learning","author":"baker","year":"2016","journal-title":"arXiv 1611 02167"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref2","article-title":"Hierarchical representations for efficient architecture search","author":"liu","year":"2017","journal-title":"arXiv 1711 00436"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref1","article-title":"Neural architecture search with reinforcement learning","author":"zoph","year":"2016","journal-title":"arXiv 1611 01578"},{"key":"ref20","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017","journal-title":"arXiv 1706 05587"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2908991"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/978-3-030-00919-9_12","article-title":"Automatically designing CNN architectures for medical image segmentation","volume":"11046","author":"aliasghar","year":"2018","journal-title":"Mach Learn Med Imag"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2019.00035"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/6514899\/09095283.pdf?arnumber=9095283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:52:07Z","timestamp":1639770727000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9095283\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2995367","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}