{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:45Z","timestamp":1740122685912,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":["52175471"],"award-info":[{"award-number":["52175471"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MF110"],"award-info":[{"award-number":["ZR2021MF110"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-05931-y","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T15:45:00Z","timestamp":1733154300000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Domain adaptive segmentation method for mechanical assembly based on iterative loops"],"prefix":"10.1007","volume":"55","author":[{"given":"Jinlei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3185-1062","authenticated-orcid":false,"given":"Chengjun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenggang","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"issue":"1\u20132","key":"5931_CR1","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s00170-023-11878-0","volume":"128","author":"J Wang","year":"2023","unstructured":"Wang J, Chen C, Dai C (2023) A mechanical assembly monitoring method based on domain adaptive semantic segmentation. Int J Adv Manuf Technol 128(1\u20132):625\u2013637. https:\/\/doi.org\/10.1007\/s00170-023-11878-0","journal-title":"Int J Adv Manuf Technol"},{"key":"5931_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103485","volume":"131","author":"MA Zamora-Hern\u00e1ndez","year":"2021","unstructured":"Zamora-Hern\u00e1ndez MA, Castro-Vargas JA, Azorin-Lopez J, Garcia-Rodriguez J (2021) Deep learning-based visual control assistant for assembly in industry 4.0. Comput Ind 131:103485. https:\/\/doi.org\/10.1016\/j.compind.2021.103485","journal-title":"Comput Ind"},{"issue":"5019104","key":"5931_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3124044","volume":"70","author":"KJ Wang","year":"2021","unstructured":"Wang KJ, Yan YJ (2021) A smart operator assistance system using deep learning for angle measurement. IEEE Trans Instrum Meas 70(5019104):1\u201314. https:\/\/doi.org\/10.1109\/TIM.2021.3124044","journal-title":"IEEE Trans Instrum Meas"},{"key":"5931_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112499","volume":"209","author":"C Chen","year":"2023","unstructured":"Chen C, Zhang C, Wang J, Li D, Li Y, Hong J (2023) Semantic segmentation of mechanical assembly using selective kernel convolution UNet with fully connected conditional random field. Measurement 209:112499. https:\/\/doi.org\/10.1016\/j.measurement.2023.112499","journal-title":"Measurement"},{"issue":"1","key":"5931_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1108\/AA-03-2017-032","volume":"39","author":"XY Yin","year":"2019","unstructured":"Yin XY, Fan XM, Zhu WM, Liu R (2019) Synchronous AR Assembly assistance and monitoring system based on ego-centric vision. Assembly Autom 39(1):1\u201316. https:\/\/doi.org\/10.1108\/AA-03-2017-032","journal-title":"Assembly Autom"},{"issue":"4","key":"5931_CR6","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/s00138-022-01317-7","volume":"33","author":"ZW Deng","year":"2022","unstructured":"Deng ZW, Kong Q, Akira N, Yoshinaga T (2022) Hierarchical contrastive adaptation for cross-domain object detection. Mach Vis Appl 33(4):62. https:\/\/doi.org\/10.1007\/s00138-022-01317-7","journal-title":"Mach Vis Appl"},{"key":"5931_CR7","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.inffus.2021.09.011","volume":"78","author":"D Zhang","year":"2022","unstructured":"Zhang D, Ye M, Liu YG, Xiong L, Zhou LH (2022) Multi-source unsupervised domain adaptation for object detection. Inform Fusion 78:138\u2013148. https:\/\/doi.org\/10.1016\/j.inffus.2021.09.011","journal-title":"Inform Fusion"},{"issue":"3","key":"5931_CR8","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s10044-017-0664-1","volume":"21","author":"S Saxena","year":"2018","unstructured":"Saxena S, Pandey S, Khanna P (2018) A semi-supervised domain adaptation assembling approach for image classification. Pattern Anal Appl 21(3):813\u2013827. https:\/\/doi.org\/10.1007\/s10044-017-0664-1","journal-title":"Pattern Anal Appl"},{"key":"5931_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108238","volume":"121","author":"YM Yin","year":"2022","unstructured":"Yin YM, Yang Z, Hu HF, Wu XF (2022) Universal multi-source domain adaptation for image classification. Pattern Recogn 121:108238. https:\/\/doi.org\/10.1016\/j.patcog.2021.108238","journal-title":"Pattern Recogn"},{"issue":"1","key":"5931_CR10","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s11263-023-01863-1","volume":"132","author":"X Luo","year":"2024","unstructured":"Luo X, Chen W, Liang ZF, Yang LQ, Wang SW, Li C (2024) Crots: cross-domain Teacher-Student Learning for source-free domain adaptive semantic segmentation. Int J Comput Vision 132(1):20\u201339. https:\/\/doi.org\/10.1007\/s11263-023-01863-1","journal-title":"Int J Comput Vision"},{"issue":"6","key":"5931_CR11","doi-asserted-by":"publisher","first-page":"3798","DOI":"10.1109\/TCSVT.2021.3116210","volume":"32","author":"YJ Tian","year":"2022","unstructured":"Tian YJ, Zhu SY (2022) Partial domain adaptation on semantic segmentation. IEEE Trans Circuits Syst Video Technol 32(6):3798\u20133809. https:\/\/doi.org\/10.1109\/TCSVT.2021.3116210","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"doi-asserted-by":"publisher","unstructured":"Hoffman J et al (2018) Cycada: cycle-consistent adversarial domain adaptation. International conference on machine learning 80:1989\u20131998. https:\/\/doi.org\/10.48550\/arXiv.1711.03213","key":"5931_CR12","DOI":"10.48550\/arXiv.1711.03213"},{"issue":"7","key":"5931_CR13","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.1109\/TMI.2021.3066683","volume":"40","author":"S Vesal","year":"2021","unstructured":"Vesal S, Gu M, Kosti R, Maier A, Ravikumar N (2021) Adapt everywhere: unsupervised adaptation of point-clouds and entropy minimization for multi-modal cardiac image segmentation. IEEE Trans Med Imaging 40(7):1838\u20131851. https:\/\/doi.org\/10.1109\/TMI.2021.3066683","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"5931_CR14","doi-asserted-by":"publisher","first-page":"3880","DOI":"10.1007\/s10489-020-01956-1","volume":"51","author":"CL Chen","year":"2021","unstructured":"Chen CL, Wang G (2021) IOSUDA: an unsupervised domain adaptation with input and output space alignment for joint optic disc and cup segmentation. Appl Intell 51(6):3880\u20133898. https:\/\/doi.org\/10.1007\/s10489-020-01956-1","journal-title":"Appl Intell"},{"key":"5931_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109299","volume":"137","author":"S Hu","year":"2023","unstructured":"Hu S, Bonardi F, Bouchafa S, Sidib\u00e9 D (2023) Multi-modal unsupervised domain adaptation for semantic image segmentation. Pattern Recogn 137:109299. https:\/\/doi.org\/10.1016\/j.patcog.2022.109299","journal-title":"Pattern Recogn"},{"issue":"9","key":"5931_CR16","doi-asserted-by":"publisher","first-page":"9667","DOI":"10.1109\/TII.2022.3233654","volume":"19","author":"S Ma","year":"2023","unstructured":"Ma S, Song K, Niu M, Tian H, Wang Y, Yan Y (2023) Shape consistent one-shot unsupervised domain adaptation for rail surface defect segmentation. Ieee Trans Industrial Inform 19(9):9667\u20139679. https:\/\/doi.org\/10.1109\/TII.2022.3233654","journal-title":"Ieee Trans Industrial Inform"},{"key":"5931_CR17","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.isprsjprs.2021.04.012","volume":"176","author":"W Liu","year":"2021","unstructured":"Liu W et al (2021) Adversarial unsupervised domain adaptation for 3D semantic segmentation with multi-modal learning. ISPRS J Photogrammetry Remote Sens 176:211\u2013221. https:\/\/doi.org\/10.1016\/j.isprsjprs.2021.04.012","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"5931_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108384","volume":"123","author":"J Huang","year":"2022","unstructured":"Huang J, Guan D, Xiao A, Lu S (2022) Multi-level adversarial network for domain adaptive semantic segmentation. Pattern Recogn 123:108384. https:\/\/doi.org\/10.1016\/j.patcog.2021.108384","journal-title":"Pattern Recogn"},{"issue":"10","key":"5931_CR19","doi-asserted-by":"publisher","first-page":"7019","DOI":"10.1109\/TCSVT.2022.3179021","volume":"32","author":"YY Zhao","year":"2022","unstructured":"Zhao YY, Zhong Z, Luo ZM, Lee GH, Sebe N (2022) Source-free open compound domain adaptation in semantic segmentation. IEEE Trans Circuits Syst Video Technol 32(10):7019\u20137032. https:\/\/doi.org\/10.1109\/TCSVT.2022.3179021","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"8","key":"5931_CR20","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1007\/s11263-021-01479-3","volume":"129","author":"SC Zhao","year":"2021","unstructured":"Zhao SC, Li B, Xu PF, Yue XY, Ding GG, Keutzer K (2021) MADAN: multi-source adversarial domain Aggregation Network for Domain Adaptation. Int J Comput Vision 129(8):2399\u20132424. https:\/\/doi.org\/10.1007\/s11263-021-01479-3","journal-title":"Int J Comput Vision"},{"key":"5931_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112818","volume":"214","author":"W Cao","year":"2023","unstructured":"Cao W et al (2023) A two-stage domain alignment method for multi-source domain fault diagnosis. Measurement 214:112818. https:\/\/doi.org\/10.1016\/j.measurement.2023.112818","journal-title":"Measurement"},{"key":"5931_CR22","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-030-01219-9_18","volume":"11207","author":"Y Zou","year":"2018","unstructured":"Zou Y, Yu Z, Kumar B, Wang J (2018) Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. Proc Eur Conf Comput Vis 11207:289\u2013305. https:\/\/doi.org\/10.1007\/978-3-030-01219-9_18","journal-title":"Proc Eur Conf Comput Vis"},{"issue":"8","key":"5931_CR23","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1007\/s11263-023-01799-6","volume":"131","author":"L Hoyer","year":"2023","unstructured":"Hoyer L, Dai DX, Wang Q, Chen YH, Van Gool L (2023) Improving semi-supervised and domain-adaptive semantic segmentation with self-supervised depth estimation. Int J Comput Vision 131(8):2070\u20132096. https:\/\/doi.org\/10.1007\/s11263-023-01799-6","journal-title":"Int J Comput Vision"},{"issue":"7","key":"5931_CR24","doi-asserted-by":"publisher","first-page":"9004","DOI":"10.1109\/TPAMI.2023.3237740","volume":"45","author":"BH Xie","year":"2023","unstructured":"Xie BH, Li S, Li MJ, Liu CH, Huang G, Wang GR (2023) SePiCo: semantic-guided pixel contrast for Domain Adaptive Semantic Segmentation. IEEE Trans Pattern Anal Mach Intell 45(7):9004\u20139021. https:\/\/doi.org\/10.1109\/TPAMI.2023.3237740","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5931_CR25","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1007\/978-3-030-58583-9_29","volume":"12372","author":"J Yang","year":"2020","unstructured":"Yang J, An W, Wang S, Zhu X, Yan C, Huang J (2020) Label-driven reconstruction for domain adaptation in semantic segmentation. Proc Eur Conf Comput Vis 12372:480\u2013498. https:\/\/doi.org\/10.1007\/978-3-030-58583-9_29","journal-title":"Proc Eur Conf Comput Vis"},{"issue":"8","key":"5931_CR26","doi-asserted-by":"publisher","first-page":"9339","DOI":"10.1109\/TPAMI.2023.3248294","volume":"45","author":"YT Cheng","year":"2023","unstructured":"Cheng YT, Wei FY, Bao JM, Chen D, Zhang WQ (2023) ADPL: adaptive dual path learning for Domain Adaptation of Semantic Segmentation. IEEE Trans Pattern Anal Mach Intell 45(8):9339\u20139356. https:\/\/doi.org\/10.1109\/TPAMI.2023.3248294","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5931_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3104032","volume":"60","author":"LF Zhang","year":"2022","unstructured":"Zhang LF, Lan M, Zhang J, Tao DC (2022) Stagewise unsupervised domain adaptation with adversarial self-training for Road Segmentation of Remote-sensing images. IEEE Trans Geosci Remote Sens 60:1\u201313. https:\/\/doi.org\/10.1109\/TGRS.2021.3104032","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5931_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108729","volume":"121","author":"J Hong","year":"2022","unstructured":"Hong J, Yu S, Chen W (2022) Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning. Appl Soft Comput 121:108729. https:\/\/doi.org\/10.1016\/j.asoc.2022.108729","journal-title":"Appl Soft Comput"},{"doi-asserted-by":"publisher","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of international conference on computer vision pp 2242\u20132251. https:\/\/doi.org\/10.1109\/ICCV.2017.244","key":"5931_CR29","DOI":"10.1109\/ICCV.2017.244"},{"key":"5931_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00366-023-01852-5","volume":"1\u201316","author":"A Abbaszadeh Shahri","year":"2023","unstructured":"Abbaszadeh Shahri A, Chunling S, Larsson S (2023) A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis. Engineering with Computers 1\u201316:1. https:\/\/doi.org\/10.1007\/s00366-023-01852-5","journal-title":"Engineering with Computers"},{"key":"5931_CR31","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","volume":"76","author":"M Abdar","year":"2021","unstructured":"Abdar M et al (2021) A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inform Fusion 76:243\u2013297. https:\/\/doi.org\/10.1016\/j.inffus.2021.05.008","journal-title":"Inform Fusion"},{"key":"5931_CR32","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.ins.2019.03.036","volume":"490","author":"MJ Patwary","year":"2019","unstructured":"Patwary MJ, Wang XZ (2019) Sensitivity analysis on initial classifier accuracy in fuzziness based semi-supervised learning. Inf Sci 490:93\u2013112. https:\/\/doi.org\/10.1016\/j.ins.2019.03.036","journal-title":"Inf Sci"},{"issue":"3","key":"5931_CR33","doi-asserted-by":"publisher","first-page":"562","DOI":"10.2166\/hydro.2020.098","volume":"22","author":"R Asheghi","year":"2020","unstructured":"Asheghi R, Hosseini SA, Saneie M, Abbaszadeh Shahri A (2020) Updating the neural network sediment load models using different sensitivity analysis methods: a regional application. J Hydroinformatics 22(3):562\u2013577. https:\/\/doi.org\/10.2166\/hydro.2020.098","journal-title":"J Hydroinformatics"},{"issue":"1","key":"5931_CR34","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/s40537-021-00515-w","volume":"8","author":"DL Naik","year":"2021","unstructured":"Naik DL, Kiran R (2021) A novel sensitivity-based method for feature selection. J Big Data 8(1):128. https:\/\/doi.org\/10.1186\/s40537-021-00515-w","journal-title":"J Big Data"},{"issue":"7","key":"5931_CR35","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1364\/BOE.9.003244","volume":"9","author":"DS Krishna","year":"2018","unstructured":"Krishna DS et al (2018) DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomedical Opt Express 9(7):3244\u20133265. https:\/\/doi.org\/10.1364\/BOE.9.003244","journal-title":"Biomedical Opt Express"},{"key":"5931_CR36","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume":"9351","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Proc Med Image Comput Computer-Assisted Intervention 9351:234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","journal-title":"Proc Med Image Comput Computer-Assisted Intervention"},{"doi-asserted-by":"publisher","unstructured":"Wang J, Zheng Z, Ma A, Lu X, Zhong Y (2021) LoveDA: a remote sensing land-cover dataset for domain adaptive semantic segmentation. arXiv preprint arXiv:2110.08733. https:\/\/doi.org\/10.48550\/arXiv.2110.08733","key":"5931_CR37","DOI":"10.48550\/arXiv.2110.08733"},{"doi-asserted-by":"publisher","unstructured":"Hoyer L, Dai D, Van Gool L (2022) Daformer: improving network architectures and training strategies for domain-adaptive semantic segmentation. Proceedings of Computer Vision and Pattern Recognition pp 9924\u20139935. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00969","key":"5931_CR38","DOI":"10.1109\/CVPR52688.2022.00969"},{"doi-asserted-by":"publisher","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Zhou Y (2021) Transunet: transformers make strong encoders for medical image segmentation. arXiv Preprint arXiv:2102 04306. https:\/\/doi.org\/10.48550\/arXiv.2102.04306","key":"5931_CR39","DOI":"10.48550\/arXiv.2102.04306"},{"key":"5931_CR40","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.5555\/3454287.3454462","volume":"32175","author":"X Wang","year":"2019","unstructured":"Wang X, Jin Y, Long M, Wang J, Jordan MI (2019) Transferable normalization: towards improving transferability of deep neural networks. Adv Neural Inf Process Syst 32175:1953\u20131963. https:\/\/doi.org\/10.5555\/3454287.3454462","journal-title":"Adv Neural Inf Process Syst"},{"issue":"13","key":"5931_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/en15134602","volume":"15","author":"SP Mirfallah Lialestani","year":"2022","unstructured":"Mirfallah Lialestani SP, Parcerisa D, Himi M, Abbaszadeh Shahri A (2022) Generating 3D geothermal maps in Catalonia, Spain using a hybrid adaptive multitask deep learning procedure. Energies 15(13):4602. https:\/\/doi.org\/10.3390\/en15134602","journal-title":"Energies"},{"issue":"1","key":"5931_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J big data 6(1):1\u201348. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J big data"},{"key":"5931_CR43","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.jmsy.2020.04.016","volume":"55","author":"K Zhang","year":"2020","unstructured":"Zhang K, Chen J, Zhang T, Zhou Z (2020) A compact convolutional neural network augmented with multiscale feature extraction of acquired monitoring data for mechanical intelligent fault diagnosis. J Manuf Syst 55:273\u2013284. https:\/\/doi.org\/10.1016\/j.jmsy.2020.04.016","journal-title":"J Manuf Syst"},{"doi-asserted-by":"publisher","unstructured":"Liu Z et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of international conference on computer vision pp 10012\u201310022. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","key":"5931_CR44","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"5931_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2023.3253472","volume":"1\u201315","author":"Z Zhong","year":"2023","unstructured":"Zhong Z, Liu X, Jiang J, Zhao D, Ji X (2023) Deep attentional guided image filtering. Ieee Trans Neural Networks Learn Syst 1\u201315:1. https:\/\/doi.org\/10.1109\/TNNLS.2023.3253472","journal-title":"Ieee Trans Neural Networks Learn Syst"},{"key":"5931_CR46","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1007\/s11390-018-1859-7","volume":"33","author":"BJ Zou","year":"2018","unstructured":"Zou BJ et al (2018) 3D filtering by block matching and convolutional neural network for image denoising. J Comput Sci Technol 33:838\u2013848. https:\/\/doi.org\/10.1007\/s11390-018-1859-7","journal-title":"J Comput Sci Technol"},{"doi-asserted-by":"publisher","unstructured":"Skorokhodov I, Tulyakov S, Elhoseiny M (2022) Stylegan-v:a continuous video generator with the price, image quality and perks of stylegan2. Proc Comput Vis Pattern Recognit 3626\u20133636. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00361","key":"5931_CR47","DOI":"10.1109\/CVPR52688.2022.00361"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05931-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05931-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05931-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T15:16:56Z","timestamp":1735831016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05931-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["5931"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05931-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"16 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All data used in this paper conforms to the Ethical and informed consent specification.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"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":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"73"}}