{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T20:05:48Z","timestamp":1762718748568,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62071060"],"award-info":[{"award-number":["No.62071060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Key Laboratory of Work Safety and Intelligent Monitoring Foundation"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00138-023-01411-4","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T12:02:16Z","timestamp":1688040136000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CLDM: convolutional layer dropout module"],"prefix":"10.1007","volume":"34","author":[{"given":"Jiafeng","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Xiang","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Tan","family":"Yue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9685-2571","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"1411_CR1","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1411_CR2","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91\u201399 (2015)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1411_CR3","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/s11263-019-01283-0","volume":"128","author":"J Park","year":"2020","unstructured":"Park, J., Woo, S., Lee, J.Y., Kweon, I.S.: A simple and light-weight attention module for convolutional neural networks. Int. J. Comput. Vis. 128, 783 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"1411_CR4","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1411_CR5","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1411_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1411_CR7","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)","DOI":"10.5244\/C.30.87"},{"key":"1411_CR8","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"1411_CR9","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"1411_CR10","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"key":"1411_CR11","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1411_CR12","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2018.03.080","volume":"328","author":"Q Xu","year":"2019","unstructured":"Xu, Q., Zhang, M., Gu, Z., Pan, G.: Overfitting remedy by sparsifying regularization on fully-connected layers of cnns. Neurocomputing 328, 69\u201374 (2019)","journal-title":"Neurocomputing"},{"issue":"1","key":"1411_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci0342472","volume":"44","author":"DM Hawkins","year":"2004","unstructured":"Hawkins, D.M.: The problem of overfitting. J. Chem. Inf. Comput. Sci. 44(1), 1\u201312 (2004)","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"1411_CR14","first-page":"950","volume":"4","author":"A Krogh","year":"1992","unstructured":"Krogh, A., Hertz, J.: A simple weight decay can improve generalization. Adv. Neural Inf. Process. Syst. 4, 950\u2013957 (1992)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1411_CR15","unstructured":"Simard, P.Y., Steinkraus, D., Platt, J.C., : Best practices for convolutional neural networks applied to visual document analysis. In: Icdar, vol. 3 (2003)"},{"key":"1411_CR16","doi-asserted-by":"crossref","unstructured":"Bottou, L.: Stochastic gradient descent tricks. In: Neural Networks: tricks of the trade, pp. 421\u2013436 (2012)","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"1411_CR17","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456, (2015)"},{"issue":"4","key":"1411_CR18","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/S0893-6080(98)00010-0","volume":"11","author":"L Prechelt","year":"1998","unstructured":"Prechelt, L.: Automatic early stopping using cross validation: quantifying the criteria. Neural Netw. 11(4), 761\u2013767 (1998)","journal-title":"Neural Netw."},{"issue":"1","key":"1411_CR19","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"1411_CR20","unstructured":"Wan, L., Zeiler, M., Zhang, S., Le\u00a0Cun, Y., Fergus, R.: Regularization of neural networks using dropconnect. In: International Conference on Machine Learning, pp. 1058\u20131066 (2013)"},{"key":"1411_CR21","first-page":"3084","volume":"26","author":"J Ba","year":"2013","unstructured":"Ba, J., Frey, B.: Adaptive dropout for training deep neural networks. Adv. Neural Inf. Process. Syst. 26, 3084\u20133092 (2013)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1411_CR22","unstructured":"Ghiasi, G., Lin, T.-Y., Le, Q.V.: Dropblock: A regularization method for convolutional networks. arXiv preprint arXiv:1810.12890 (2018)"},{"key":"1411_CR23","doi-asserted-by":"publisher","first-page":"62830","DOI":"10.1109\/ACCESS.2020.2983774","volume":"8","author":"L Qian","year":"2020","unstructured":"Qian, L., Hu, L., Zhao, L., Wang, T., Jiang, R.: Sequence-dropout block for reducing overfitting problem in image classification. IEEE Access 8, 62830\u201362840 (2020)","journal-title":"IEEE Access"},{"key":"1411_CR24","doi-asserted-by":"crossref","unstructured":"Hou, S., Wang, Z.: Weighted channel dropout for regularization of deep convolutional neural network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8425\u20138432 (2019)","DOI":"10.1609\/aaai.v33i01.33018425"},{"key":"1411_CR25","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neucom.2020.02.007","volume":"394","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Du, J., Wang, M., Ge, S.S.: Adcm: attention dropout convolutional module. Neurocomputing 394, 95\u2013104 (2020)","journal-title":"Neurocomputing"},{"key":"1411_CR26","unstructured":"Xie, T., Liu, M., Deng, J., Cheng, X., Wang, X., Liu, M.: Focuseddropout for convolutional neural network. CoRR arXiv:2103.15425 (2021)"},{"key":"1411_CR27","unstructured":"Wei, Y.: Variance, entropy and uncertainty measure. (1987)"},{"issue":"4","key":"1411_CR28","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433\u2013459 (2010)","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"1411_CR29","doi-asserted-by":"crossref","unstructured":"Wallisch, P.: Chapter 20 - information theory. In: MATLAB for Neuroscientists (Second Edition), pp. 317\u2013327 (2014)","DOI":"10.1016\/B978-0-12-383836-0.00018-7"},{"key":"1411_CR30","unstructured":"Wang, S., Manning, C.: Fast dropout training. In: International Conference on Machine Learning, pp. 118\u2013126 (2013). PMLR"},{"key":"1411_CR31","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"1411_CR32","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, pp. 648\u2013656 (2015)","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"1411_CR33","doi-asserted-by":"publisher","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.: Correlation-based structural dropout for convolutional neural networks. Pattern Recogn. 120, 108117 (2021)","journal-title":"Pattern Recogn."},{"issue":"6","key":"1411_CR34","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L.: The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process. Mag. 29(6), 141\u2013142 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"1411_CR35","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"1411_CR36","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"1411_CR37","unstructured":"Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics JMLR Workshop and Conference Proceedings, pp. 215\u2013223 (2011)"},{"key":"1411_CR38","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)"},{"issue":"7","key":"1411_CR39","first-page":"3","volume":"7","author":"Y Le","year":"2015","unstructured":"Le, Y., Yang, X.: Tiny imagenet visual recognition challenge. CS 231N 7(7), 3 (2015)","journal-title":"CS 231N"},{"issue":"1","key":"1411_CR40","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s00138-018-0966-3","volume":"30","author":"E Bayraktar","year":"2019","unstructured":"Bayraktar, E., Yigit, C.B., Boyraz, P.: A hybrid image dataset toward bridging the gap between real and simulation environments for robotics: Annotated desktop objects real and synthetic images dataset: Adoreset. Mach. Vis. Appl. 30(1), 23\u201340 (2019)","journal-title":"Mach. Vis. Appl."},{"key":"1411_CR41","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3234\u20133243 (2016)","DOI":"10.1109\/CVPR.2016.352"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01411-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-023-01411-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-023-01411-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T19:08:02Z","timestamp":1689880082000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-023-01411-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["1411"],"URL":"https:\/\/doi.org\/10.1007\/s00138-023-01411-4","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2023,6,29]]},"assertion":[{"value":"20 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No competing financial and non-financial interests exist.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"63"}}