{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T17:04:56Z","timestamp":1769015096772,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,5,21]],"date-time":"2023-05-21T00:00:00Z","timestamp":1684627200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,21]],"date-time":"2023-05-21T00:00:00Z","timestamp":1684627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11293-2","type":"journal-article","created":{"date-parts":[[2023,5,21]],"date-time":"2023-05-21T06:01:11Z","timestamp":1684648871000},"page":"7985-7997","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improving Regularization in Deep Neural Networks by Co-adaptation Trace Detection"],"prefix":"10.1007","volume":"55","author":[{"given":"Hojjat","family":"Moayed","sequence":"first","affiliation":[]},{"given":"Eghbal G.","family":"Mansoori","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,21]]},"reference":[{"key":"11293_CR1","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.neucom.2019.02.056","volume":"347","author":"S Mahdavifar","year":"2019","unstructured":"Mahdavifar S, Ghorbani AA (2019) Application of deep learning to cybersecurity: a survey. Neurocomputing 347:149\u2013176","journal-title":"Neurocomputing"},{"key":"11293_CR2","doi-asserted-by":"publisher","first-page":"1706","DOI":"10.1016\/j.procs.2018.05.144","volume":"132","author":"AR Pathak","year":"2018","unstructured":"Pathak AR, Pandey M, Rautaray S (2018) Application of deep learning for object detection. Procedia Comput Sci 132:1706\u20131717","journal-title":"Procedia Comput Sci"},{"key":"11293_CR3","doi-asserted-by":"publisher","first-page":"3947","DOI":"10.1007\/s10462-019-09784-7","volume":"53","author":"R Moradi","year":"2020","unstructured":"Moradi R, Berangi R, Minaei B (2020) A survey of regularization strategies for deep models. Artif Intell Rev 53:3947\u20133986","journal-title":"Artif Intell Rev"},{"key":"11293_CR4","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"11293_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00492-0","volume":"8","author":"C Shorten","year":"2021","unstructured":"Shorten C, Khoshgoftaar TM, Furht B (2021) Text data augmentation for deep learning. J Big Data 8:1\u201334","journal-title":"J Big Data"},{"key":"11293_CR6","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","volume":"461","author":"T Liang","year":"2021","unstructured":"Liang T, Glossner J, Wang L, Shi S, Zhang X (2021) Pruning and quantization for deep neural network acceleration: a survey. Neurocomputing 461:370\u2013403","journal-title":"Neurocomputing"},{"key":"11293_CR7","unstructured":"GE Hinton, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"key":"11293_CR8","unstructured":"Z Zhang, Dalca AV, Sabuncu MR (2019) Confidence calibration for convolutional neural networks using structured dropout. arXiv preprint arXiv:1906.09551"},{"key":"11293_CR9","unstructured":"Wan L, Zeiler M, Zhang S, Le Cun Y, Fergus R Regularization of neural networks using dropconnect. In: International conference on machine learning, 2013. PMLR, pp 1058\u20131066"},{"key":"11293_CR10","doi-asserted-by":"crossref","unstructured":"Tompson J, Goroshin R, Jain A, LeCun Y, Bregler C Efficient object localization using convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. pp 648\u2013656","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"11293_CR11","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1007\/s11063-019-10147-0","volume":"51","author":"H Pan","year":"2020","unstructured":"Pan H, Niu X, Li R, Shen S, Dou Y (2020) Dropfilterr: A novel regularization method for learning convolutional neural networks. Neural Process Lett 51:1285\u20131298","journal-title":"Neural Process Lett"},{"key":"11293_CR12","unstructured":"Sun C, Sharma J, Maiti M (2021) Investigating the Relationship Between Dropout Regularization and Model Complexity in Neural Networks. arXiv preprint arXiv:2108.06628"},{"key":"11293_CR13","first-page":"4267","volume":"32","author":"H Li","year":"2021","unstructured":"Li H, Weng J, Mao Y, Wang Y, Zhan Y, Cai Q, Gu W (2021) Adaptive dropout method based on biological principles. IEEE T Neur Net Lear 32:4267\u20134276","journal-title":"IEEE T Neur Net Lear"},{"key":"11293_CR14","doi-asserted-by":"crossref","unstructured":"Yamashita T, Tanaka M, Yamauchi Y, Fujiyoshi H SWAP-NODE: A regularization approach for deep convolutional neural networks. In: 2015 IEEE International Conference on Image Processing (ICIP), 2015. IEEE, pp 2475\u20132479","DOI":"10.1109\/ICIP.2015.7351247"},{"key":"11293_CR15","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1109\/TPAMI.2017.2701831","volume":"40","author":"G Kang","year":"2017","unstructured":"Kang G, Li J, Tao D (2017) Shakeout: a new approach to regularized deep neural network training. IEEE T Pattern Anal 40:1245\u20131258","journal-title":"IEEE T Pattern Anal"},{"key":"11293_CR16","unstructured":"Zhang C, Bengio S, Singer Y (2019) Are all layers created equal? arXiv preprint arXiv:1902.01996"},{"key":"11293_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-88197-5","volume":"11","author":"C Moodley","year":"2021","unstructured":"Moodley C, Sephton B, Rodr\u00edguez-Fajardo V, Forbes A (2021) Deep learning early stopping for non-degenerate ghost imaging. Sci Rep 11:1\u20139","journal-title":"Sci Rep"},{"key":"11293_CR18","unstructured":"De Vries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552"},{"key":"11293_CR19","unstructured":"Ghiasi G, Lin T-Y, Le QV (2018) Dropblock: A regularization method for convolutional networks. Adv Neur In 31"},{"key":"11293_CR20","doi-asserted-by":"crossref","unstructured":"Pham H, Le QV (2021) Autodropout: Learning dropout patterns to regularize deep networks. arXiv preprint arXiv:2101.01761 1:3","DOI":"10.1609\/aaai.v35i11.17127"},{"key":"11293_CR21","doi-asserted-by":"crossref","unstructured":"Rennie SJ, Goel V, Thomas S Annealed dropout training of deep networks. In: 2014 IEEE Spoken Language Technology Workshop (SLT), 2014. IEEE, pp 159\u2013164","DOI":"10.1109\/SLT.2014.7078567"},{"key":"11293_CR22","doi-asserted-by":"publisher","first-page":"36140","DOI":"10.1109\/ACCESS.2019.2904881","volume":"7","author":"L Liu","year":"2019","unstructured":"Liu L, Luo Y, Shen X, Sun M, Li B (2019) \u03b2-dropout: a unified dropout. IEEE Access 7:36140\u201336153","journal-title":"IEEE Access"},{"key":"11293_CR23","doi-asserted-by":"crossref","unstructured":"Park S, Kwak N Analysis on the dropout effect in convolutional neural networks. In: Asian conference on computer vision, 2016. Springer, pp 189-204","DOI":"10.1007\/978-3-319-54184-6_12"},{"key":"11293_CR24","unstructured":"Gomez AN, Zhang I, Kamalakara SR, Madaan D, Swersky K, Gal Y, Hinton GE (2019) Learning sparse networks using targeted dropout. arXiv preprint arXiv:1905.13678"},{"key":"11293_CR25","doi-asserted-by":"publisher","first-page":"108117","DOI":"10.1016\/j.patcog.2021.108117","volume":"120","author":"Y Zeng","year":"2021","unstructured":"Zeng Y, Dai T, Chen B, Xia S-T, Lu J (2021) Correlation-based structural dropout for convolutional neural networks. Pattern Recogn 120:108117","journal-title":"Pattern Recogn"},{"key":"11293_CR26","unstructured":"Wu L, Li J, Wang Y, Meng Q, Qin T, Chen W, Zhang M, Liu T-Y (2021) R-drop: regularized dropout for neural networks. Adv Neur In 34"},{"key":"11293_CR27","first-page":"5279","volume":"33","author":"H Salehinejad","year":"2021","unstructured":"Salehinejad H, Valaee S (2021) Edropout: energy-based dropout and pruning of deep neural networks. IEEE T Neur Net Lear 33:5279\u20135292","journal-title":"IEEE T Neur Net Lear"},{"key":"11293_CR28","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278\u20132324","journal-title":"Proc IEEE"},{"key":"11293_CR29","unstructured":"Y Netzer, Wang T, Coates A, Bissacco A, Wu B, Ng AY.(2011).Reading digits in natural images with unsupervised feature learning http:\/\/ai.stanford.edu\/~twangcat\/papers\/nips2011_housenumbers.pdf."},{"key":"11293_CR30","unstructured":"A Krizhevsky, Hinton G (2009) Learning multiple layers of features from tiny images."},{"key":"11293_CR31","unstructured":"Wu J, Zhang Q, Xu G (2017) Tiny imagenet challenge. Technical report"},{"key":"11293_CR32","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"11293_CR33","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11293_CR34","first-page":"1","volume":"32","author":"H Touvron","year":"2019","unstructured":"Touvron H, Vedaldi A, Douze M, J\u00e9gou H (2019) Fixing the train-test resolution discrepancy. Adv Neur In 32:1\u201311","journal-title":"Adv Neur In"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11293-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11293-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11293-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T19:16:25Z","timestamp":1698520585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11293-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,21]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11293"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11293-2","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,21]]},"assertion":[{"value":"1 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}