{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T02:49:55Z","timestamp":1779245395624,"version":"3.51.4"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant"},{"name":"Korean Government through the Ministry of Science and ICT","award":["2021-0-00106"],"award-info":[{"award-number":["2021-0-00106"]}]},{"name":"Korean Government through the Ministry of Science and ICT","award":["2022-0-00971"],"award-info":[{"award-number":["2022-0-00971"]}]},{"DOI":"10.13039\/501100002573","name":"2020 Yonsei University Future-Leading Research Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002573","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3190643","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T15:30:57Z","timestamp":1657726257000},"page":"76044-76054","source":"Crossref","is-referenced-by-count":5,"title":["Checkerboard Dropout: A Structured Dropout With Checkerboard Pattern for Convolutional Neural Networks"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9948-1400","authenticated-orcid":false,"given":"Khanh-Binh","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaehyuk","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Semiconductor Systems Engineering, Sungkyunkwan University, Suwon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1502-5353","authenticated-orcid":false,"given":"Joon-Sung","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref2","first-page":"173","article-title":"Deep speech 2: End-to-end speech recognition in English and Mandarin","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Amodei"},{"key":"ref3","first-page":"5","article-title":"Deep learning for NLP (without magic)","volume-title":"Proc. Tutorial Abstr. ACL","author":"Socher"},{"key":"ref4","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref7","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015","journal-title":"arXiv:1502.03167"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref11","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","volume":"97","author":"Tan"},{"issue":"1","key":"ref12","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":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-54184-6_12"},{"key":"ref15","first-page":"627","article-title":"Removing the feature correlation effect of multiplicative noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref16","first-page":"99","article-title":"Slow, decorrelated features for pretraining complex cell-like networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"22","author":"Bengio"},{"key":"ref17","article-title":"All you need is a good init","author":"Mishkin","year":"2015","journal-title":"arXiv:1511.06422"},{"key":"ref18","article-title":"Reducing overfitting in deep networks by decorrelating representations","author":"Cogswell","year":"2015","journal-title":"arXiv:1511.06068"},{"key":"ref19","article-title":"Regularizing CNNs with locally constrained decorrelations","author":"Rodr\u00edguez","year":"2016","journal-title":"arXiv:1611.01967"},{"key":"ref20","first-page":"10727","article-title":"Dropblock: A regularization method for convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ghiasi"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1111\/j.1538-4632.1995.tb00912.x"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2307\/2332142"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref25","first-page":"1058","article-title":"Regularization of neural networks using dropconnect","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wan"},{"key":"ref26","article-title":"FractalNet: Ultra-deep neural networks without residuals","author":"Larsson","year":"2016","journal-title":"arXiv:1605.07648"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2018.2890236"},{"key":"ref28","first-page":"1319","article-title":"Maxout networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Goodfellow"},{"key":"ref29","article-title":"Shake-shake regularization","author":"Gastaldi","year":"2017","journal-title":"arXiv:1705.07485"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2960566"},{"key":"ref31","article-title":"AutoAugment: Learning augmentation policies from data","author":"Cubuk","year":"2018","journal-title":"arXiv:1805.09501"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17127"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.2307\/2986645"},{"key":"ref35","first-page":"32","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref36","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(98)00116-6"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-17943-7_117"},{"key":"ref39","article-title":"Accurate, large minibatch SGD: Training ImageNet in 1 hour","author":"Goyal","year":"2017","journal-title":"arXiv:1706.02677"},{"key":"ref40","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016","journal-title":"arXiv:1608.03983"},{"key":"ref41","first-page":"4694","article-title":"When does label smoothing help?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"M\u00fcller"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"ref43","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1710.09412"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6057"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09828384.pdf?arnumber=9828384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T06:00:23Z","timestamp":1769493623000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9828384\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3190643","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}