{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:41:06Z","timestamp":1772862066520,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"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 Comput & Applic"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00521-024-09434-2","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T10:02:32Z","timestamp":1708077752000},"page":"6855-6873","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-label noisy samples in underwater inspection from the oil and gas industry"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4231-0667","authenticated-orcid":false,"given":"First Vitor","family":"Sousa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Second Amanda","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Third Manoela","family":"Koher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fourth Marco","family":"Pacheco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"issue":"1","key":"9434_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40662-022-00277-3","volume":"9","author":"A You","year":"2022","unstructured":"You A, Kim JK, Ryu IH, Yoo TK (2022) Application of generative adversarial networks (gan) for ophthalmology image domains: a survey. Eye Vis 9(1):1\u201319","journal-title":"Eye Vis"},{"issue":"1","key":"9434_CR2","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1038\/s41592-021-01360-8","volume":"19","author":"M Baek","year":"2022","unstructured":"Baek M, Baker D (2022) Deep learning and protein structure modeling. Nature Methods 19(1):13\u201314","journal-title":"Nature Methods"},{"issue":"4","key":"9434_CR3","doi-asserted-by":"publisher","first-page":"683","DOI":"10.3390\/diagnostics13040683","volume":"13","author":"M Yusoff","year":"2023","unstructured":"Yusoff M, Haryanto T, Suhartanto H, Mustafa WA, Zain JM, Kusmardi K (2023) Accuracy analysis of deep learning methods in breast cancer classification: A structured review. Diagnostics 13(4):683","journal-title":"Diagnostics"},{"key":"9434_CR4","first-page":"535","volume":"23","author":"J Yu","year":"2023","unstructured":"Yu J, Yin H, Xia X, Chen T, Li J, Huang Z (2023) Self-supervised learning for recommender systems: a survey. IEEE Trans Knowl Data Eng 23:535","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"9434_CR5","doi-asserted-by":"publisher","first-page":"7955","DOI":"10.1109\/TPAMI.2021.3119334","volume":"44","author":"W Liu","year":"2021","unstructured":"Liu W, Wang H, Shen X, Tsang IW (2021) The emerging trends of multi-label learning. IEEE Trans Patt Anal Mach Intell 44(11):7955\u20137974","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"9434_CR6","first-page":"1","volume":"1","author":"A Haghighian Roudsari","year":"2022","unstructured":"Haghighian Roudsari A, Afshar J, Lee W, Lee S (2022) Patentnet: multi-label classification of patent documents using deep learning based language understanding. Scientometrics 1:1\u201325","journal-title":"Scientometrics"},{"key":"9434_CR7","first-page":"003","volume":"2","author":"M Kaselimi","year":"2022","unstructured":"Kaselimi M, Voulodimos A, Daskalopoulos I, Doulamis N, Doulamis A (2022) A vision transformer model for convolution-free multilabel classification of satellite imagery in deforestation monitoring. IEEE Trans Neural Netw Learn Syst 2:003","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9434_CR8","doi-asserted-by":"crossref","unstructured":"Cheng X, Lin H, Wu X, Shen D, Yang, F, Liu H, Shi N Mltr: Multi-label classification with transformer. In: 2022 IEEE international conference on multimedia and expo (ICME), pp. 1\u20136 (2022). IEEE","DOI":"10.1109\/ICME52920.2022.9860016"},{"key":"9434_CR9","first-page":"355","volume":"31","author":"B Han","year":"2018","unstructured":"Han B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I, Sugiyama M (2018) Co-teaching: Robust training of deep neural networks with extremely noisy labels. Adv Neural Inform Process Syst 31:355","journal-title":"Adv Neural Inform Process Syst"},{"key":"9434_CR10","doi-asserted-by":"crossref","unstructured":"Wei H, Feng L, Chen X, An B (2020) Combating noisy labels by agreement: A joint training method with co-regularization. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 13726\u201313735","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"9434_CR11","doi-asserted-by":"crossref","unstructured":"Yao Y, Sun Z, Zhang C, Shen F, Wu Q, Zhang J, Tang Z (2021) Jo-src: a contrastive approach for combating noisy labels. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5192\u20135201","DOI":"10.1109\/CVPR46437.2021.00515"},{"key":"9434_CR12","unstructured":"Yu X, Han B, Yao J, Niu G, Tsang I, Sugiyama M (2019) How does disagreement help generalization against label corruption? In: international conference on machine learning, pp. 7164\u20137173 PMLR"},{"key":"9434_CR13","unstructured":"Huang L, Zhang C, Zhang H (2021) Self-adaptive training: bridging the supervised and self-supervised learning. arXiv preprint arXiv:2101.08732"},{"key":"9434_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3226371","volume":"60","author":"T Burgert","year":"2022","unstructured":"Burgert T, Ravanbakhsh M, Demir B (2022) On the effects of different types of label noise in multi-label remote sensing image classification. IEEE Trans Geosci Remote Sens 60:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"9434_CR15","doi-asserted-by":"crossref","unstructured":"Sousa V, Pereira AL, Kohler M, Pacheco M Learning by small loss approach multi-label to deal with noisy labels. In: International Conference on Computational Science and Its Applications, pp. 385\u2013403 (2023). Springer","DOI":"10.1007\/978-3-031-36805-9_26"},{"key":"9434_CR16","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: computer vision\u2013ECCV 2016 workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14, pp. 850\u2013865 . Springer","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"9434_CR17","doi-asserted-by":"crossref","unstructured":"Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: proceedings of the eleventh annual conference on computational learning theory, pp. 92\u2013100","DOI":"10.1145\/279943.279962"},{"key":"9434_CR18","doi-asserted-by":"crossref","unstructured":"Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp. 270\u2013279","DOI":"10.1145\/1869790.1869829"},{"key":"9434_CR19","first-page":"1","volume":"2022","author":"S Ahlswede","year":"2022","unstructured":"Ahlswede S, Schulz C, Gava C, Helber P, Bischke B, F\u00f6rster M, Arias F, Hees J, Demir B, Kleinschmit B (2022) Treesatai benchmark archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing. Earth Syst Sci Data Discuss 2022:1\u201322","journal-title":"Earth Syst Sci Data Discuss"},{"key":"9434_CR20","unstructured":"Wei T, Shi JX, Tu WW, Li YF (2021) Robust long-tailed learning under label noise. arXiv preprint arXiv:2108.11569"},{"key":"9434_CR21","doi-asserted-by":"crossref","unstructured":"Ghosh A, Kumar H, Sastry PS (2017) Robust loss functions under label noise for deep neural networks. In: proceedings of the AAAI conference on artificial intelligence, vol. 31","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"9434_CR22","first-page":"20331","volume":"33","author":"S Liu","year":"2020","unstructured":"Liu S, Niles-Weed J, Razavian N, Fernandez-Granda C (2020) Early-learning regularization prevents memorization of noisy labels. Adv Neural Inform Process Syst 33:20331\u201320342","journal-title":"Adv Neural Inform Process Syst"},{"key":"9434_CR23","doi-asserted-by":"crossref","unstructured":"Sun Z, Shen F, Huang D, Wang Q, Shu X, Yao Y, Tang J (2022) Pnp: Robust learning from noisy labels by probabilistic noise prediction. In: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5311\u20135320","DOI":"10.1109\/CVPR52688.2022.00524"},{"key":"9434_CR24","doi-asserted-by":"crossref","unstructured":"Inoue N, Simo-Serra E, Yamasaki T, Ishikawa H (2017) Multi-label fashion image classification with minimal human supervision. In: proceedings of the IEEE international conference on computer vision workshops, pp. 2261\u20132267","DOI":"10.1109\/ICCVW.2017.265"},{"key":"9434_CR25","doi-asserted-by":"crossref","unstructured":"Hu M, Han, H, Shan S, Chen X (2019) Multi-label learning from noisy labels with non-linear feature transformation. In: computer vision\u2013ACCV 2018: 14th asian conference on computer vision, Perth, Australia, December 2\u20136, 2018, Revised Selected Papers, Part V 14, pp. 404\u2013419 Springer","DOI":"10.1007\/978-3-030-20873-8_26"},{"issue":"2","key":"9434_CR26","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0016-0032(96)00063-4","volume":"334","author":"M Men\u00e9ndez","year":"1997","unstructured":"Men\u00e9ndez M, Pardo J, Pardo L, Pardo M (1997) The jensen-shannon divergence. J Frank Inst 334(2):307\u2013318","journal-title":"J Frank Inst"},{"issue":"3","key":"9434_CR27","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2021) Understanding deep learning (still) requires rethinking generalization. Commun ACM 64(3):107\u2013115","journal-title":"Commun ACM"},{"key":"9434_CR28","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. Adv Neural Inform Process Syst 33:18661\u201318673","journal-title":"Adv Neural Inform Process Syst"},{"key":"9434_CR29","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9434_CR30","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248\u2013255. Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"9434_CR31","unstructured":"Kingma DP, Ba J (2014)Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09434-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09434-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09434-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T19:08:12Z","timestamp":1710616092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09434-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":31,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9434"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09434-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,16]]},"assertion":[{"value":"28 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors state that there is no conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}