{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T04:37:24Z","timestamp":1759639044192,"version":"3.37.3"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"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":["Appl Intell"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10489-024-05688-4","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T22:01:59Z","timestamp":1722981719000},"page":"10084-10105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel individual-relational consistency for bad semi-supervised generative adversarial networks (IRC-BSGAN) in image classification and synthesis"],"prefix":"10.1007","volume":"54","author":[{"given":"Mohammad Saber","family":"Iraji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0779-6027","authenticated-orcid":false,"given":"Jafar","family":"Tanha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad-Ali","family":"Balafar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad-Reza","family":"Feizi-Derakhshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"issue":"18","key":"5688_CR1","doi-asserted-by":"crossref","first-page":"27867","DOI":"10.1007\/s11042-021-10811-5","volume":"80","author":"MA Khan","year":"2021","unstructured":"Khan MA et al (2021) A deep survey on supervised learning based human detection and activity classification methods. Multimed Tools Appl 80(18):27867\u201327923","journal-title":"Multimed Tools Appl"},{"key":"5688_CR2","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.ins.2021.04.006","volume":"570","author":"JM Duarte","year":"2021","unstructured":"Duarte JM et al (2021) Deep analysis of word sense disambiguation via semi-supervised learning and neural word representations. Inf Sci 570:278\u2013297","journal-title":"Inf Sci"},{"key":"5688_CR3","doi-asserted-by":"crossref","first-page":"101825","DOI":"10.1016\/j.rcim.2019.101825","volume":"61","author":"Y Gao","year":"2020","unstructured":"Gao Y et al (2020) A semi-supervised convolutional neural network-based method for steel surface defect recognition. Robot Comput-Integr Manuf 61:101825","journal-title":"Robot Comput-Integr Manuf"},{"key":"5688_CR4","first-page":"18408","volume":"34","author":"B Zhang","year":"2021","unstructured":"Zhang B et al (2021) Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Adv Neural Inf Process Syst 34:18408\u201318419","journal-title":"Adv Neural Inf Process Syst"},{"key":"5688_CR5","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","volume":"145","author":"V Verma","year":"2022","unstructured":"Verma V et al (2022) Interpolation consistency training for semi-supervised learning. Neural Netw 145:90\u2013106","journal-title":"Neural Netw"},{"issue":"1","key":"5688_CR6","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10489-021-02308-3","volume":"52","author":"P Kang","year":"2022","unstructured":"Kang P et al (2022) Intra-class low-rank regularization for supervised and semi-supervised cross-modal retrieval. Appl Intell 52(1):33\u201354","journal-title":"Appl Intell"},{"issue":"3","key":"5688_CR7","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s13042-022-01658-9","volume":"14","author":"M Han","year":"2023","unstructured":"Han M et al (2023) A survey of multi-label classification based on supervised and semi-supervised learning. Int J Mach Learn Cybern 14(3):697\u2013724","journal-title":"Int J Mach Learn Cybern"},{"key":"5688_CR8","doi-asserted-by":"crossref","first-page":"110166","DOI":"10.1016\/j.knosys.2022.110166","volume":"260","author":"H Xu","year":"2023","unstructured":"Xu H et al (2023) Semi-supervised learning with pseudo-negative labels for image classification. Knowl-Based Syst 260:110166","journal-title":"Knowl-Based Syst"},{"key":"5688_CR9","doi-asserted-by":"crossref","first-page":"103788","DOI":"10.1016\/j.cviu.2023.103788","volume":"235","author":"S Li","year":"2023","unstructured":"Li S et al (2023) Robust Teacher: Self-correcting pseudo-label-guided semi-supervised learning for object detection. Comput Vis Image Underst 235:103788","journal-title":"Comput Vis Image Underst"},{"key":"5688_CR10","doi-asserted-by":"crossref","first-page":"104142","DOI":"10.1016\/j.bspc.2022.104142","volume":"79","author":"Z Peng","year":"2023","unstructured":"Peng Z et al (2023) Semi-supervised medical image classification with adaptive threshold pseudo-labeling and unreliable sample contrastive loss. Biomed Signal Process Control 79:104142","journal-title":"Biomed Signal Process Control"},{"issue":"11","key":"5688_CR11","doi-asserted-by":"crossref","first-page":"7832","DOI":"10.1109\/TCSVT.2022.3186041","volume":"32","author":"D Li","year":"2022","unstructured":"Li D, Liu Y, Song L (2022) Adaptive weighted losses with distribution approximation for efficient consistency-based semi-supervised learning. IEEE Trans Circuits Syst Video Technol 32(11):7832\u20137842","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5688_CR12","doi-asserted-by":"crossref","first-page":"102010","DOI":"10.1016\/j.media.2021.102010","volume":"70","author":"X Wang","year":"2021","unstructured":"Wang X et al (2021) Deep virtual adversarial self-training with consistency regularization for semi-supervised medical image classification. Med Image Anal 70:102010","journal-title":"Med Image Anal"},{"key":"5688_CR13","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.neunet.2023.05.006","volume":"164","author":"Y Shi","year":"2023","unstructured":"Shi Y et al (2023) Multi-granularity knowledge distillation and prototype consistency regularization for class-incremental learning. Neural Netw 164:617\u2013630","journal-title":"Neural Netw"},{"key":"5688_CR14","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neucom.2023.01.075","volume":"529","author":"L Su","year":"2023","unstructured":"Su L et al (2023) Dual consistency semi-supervised nuclei detection via global regularization and local adversarial learning. Neurocomputing 529:204\u2013213","journal-title":"Neurocomputing"},{"key":"5688_CR15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.neunet.2019.08.017","volume":"119","author":"H-K Poon","year":"2019","unstructured":"Poon H-K et al (2019) Hierarchical gated recurrent neural network with adversarial and virtual adversarial training on text classification. Neural Netw 119:299\u2013312","journal-title":"Neural Netw"},{"issue":"2","key":"5688_CR16","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s13042-021-01416-3","volume":"13","author":"Y Chen","year":"2022","unstructured":"Chen Y et al (2022) Generating robust real-time object detector with uncertainty via virtual adversarial training. Int J Mach Learn Cybern 13(2):431\u2013445","journal-title":"Int J Mach Learn Cybern"},{"key":"5688_CR17","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.ins.2023.01.074","volume":"626","author":"M Yan","year":"2023","unstructured":"Yan M, Hui SC, Li N (2023) DML-PL: Deep metric learning based pseudo-labeling framework for class imbalanced semi-supervised learning. Inf Sci 626:641\u2013657","journal-title":"Inf Sci"},{"issue":"11","key":"5688_CR18","doi-asserted-by":"crossref","first-page":"33313","DOI":"10.1007\/s11042-023-16383-w","volume":"83","author":"B Ke","year":"2024","unstructured":"Ke B, Lu H, You C, Zhu W, Xie L, Yao Y (2024) A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency. Multimedia Tools and Applications 83(11):33313\u201333331","journal-title":"Multimedia Tools and Applications"},{"key":"5688_CR19","doi-asserted-by":"crossref","first-page":"108777","DOI":"10.1016\/j.patcog.2022.108777","volume":"130","author":"Z Feng","year":"2022","unstructured":"Feng Z et al (2022) Dmt: Dynamic mutual training for semi-supervised learning. Pattern Recogn 130:108777","journal-title":"Pattern Recogn"},{"key":"5688_CR20","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.neucom.2022.07.073","volume":"506","author":"X Bi","year":"2022","unstructured":"Bi X et al (2022) Entropy-weighted reconstruction adversary and curriculum pseudo labeling for domain adaptation in semantic segmentation. Neurocomputing 506:277\u2013289","journal-title":"Neurocomputing"},{"key":"5688_CR21","doi-asserted-by":"crossref","first-page":"8441","DOI":"10.1109\/TNNLS.2022.3228380","volume":"35","author":"Y Duan","year":"2020","unstructured":"Duan Y et al (2020) Mutexmatch: semi-supervised learning with mutex-based consistency regularization. IEEE Trans Neural Netw Learn Syst 35:8441\u20138455","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"11","key":"5688_CR22","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1109\/TMI.2020.2995518","volume":"39","author":"Q Liu","year":"2020","unstructured":"Liu Q et al (2020) Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans Med Imaging 39(11):3429\u20133440","journal-title":"IEEE Trans Med Imaging"},{"key":"5688_CR23","doi-asserted-by":"crossref","unstructured":"\u00a0Luo Y, Zhu J, Li M, Ren Y, Zhang Bl (2018) Smooth neighbors on teacher graphs for semi-supervised learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp\u00a08896\u20138905","DOI":"10.1109\/CVPR.2018.00927"},{"issue":"2","key":"5688_CR24","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen JE, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109(2):373\u2013440","journal-title":"Mach Learn"},{"issue":"4","key":"5688_CR25","doi-asserted-by":"crossref","first-page":"3933","DOI":"10.1007\/s10489-022-03771-2","volume":"53","author":"X Li","year":"2023","unstructured":"Li X, Luan Y, Chen L (2023) Semi-supervised GAN with similarity constraint for mode diversity. Appl Intell 53(4):3933\u20133946","journal-title":"Appl Intell"},{"issue":"6","key":"5688_CR26","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1007\/s00371-021-02262-8","volume":"38","author":"L Wang","year":"2022","unstructured":"Wang L, Sun Y, Wang Z (2022) CCS-GAN: a semi-supervised generative adversarial network for image classification. Vis Comput 38(6):2009\u20132021","journal-title":"Vis Comput"},{"key":"5688_CR27","doi-asserted-by":"crossref","first-page":"103109","DOI":"10.1016\/j.cviu.2020.103109","volume":"202","author":"C Mayer","year":"2021","unstructured":"Mayer C, Paul M, Timofte R (2021) Adversarial feature distribution alignment for semi-supervised learning. Comput Vis Image Underst 202:103109","journal-title":"Comput Vis Image Underst"},{"key":"5688_CR28","unstructured":"Li W, Wang Z, Li J, Polson J, Speier W, Arnold CW (2019) Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach. In: CVPR Workshops, pp 1\u201311"},{"key":"5688_CR29","unstructured":"Dai Z, Yang Z, Yang F, Cohen WW, Salakhutdinov RR\u00a0(2017) Good semi-supervised learning that requires a bad gan. Adv Neural Inf Process Syst 30:4\u20136"},{"key":"5688_CR30","unstructured":"Dong J, Lin T (2019) MarginGAN: adversarial training in semi-supervised learning. Adv Neural Inf Process Syst 32:2\u20135"},{"key":"5688_CR31","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.neucom.2021.08.051","volume":"462","author":"Y Zhang","year":"2021","unstructured":"Zhang Y et al (2021) Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images. Neurocomputing 462:491\u2013505","journal-title":"Neurocomputing"},{"issue":"8","key":"5688_CR32","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1007\/s13042-023-01805-w","volume":"14","author":"Y Gong","year":"2023","unstructured":"Gong Y, Wu Q, Cheng D (2023) A co-training method based on parameter-free and single-step unlabeled data selection strategy with natural neighbors. Int J Mach Learn Cybern 14(8):2887\u20132902","journal-title":"Int J Mach Learn Cybern"},{"issue":"12","key":"5688_CR33","doi-asserted-by":"crossref","first-page":"4179","DOI":"10.1007\/s00371-021-02287-z","volume":"38","author":"Y Tian","year":"2022","unstructured":"Tian Y et al (2022) Consistency regularization teacher\u2013student semi-supervised learning method for target recognition in SAR images. Vis Comput 38(12):4179\u20134192","journal-title":"Vis Comput"},{"key":"5688_CR34","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1016\/j.neucom.2020.06.133","volume":"453","author":"J Chen","year":"2021","unstructured":"Chen J, Yang M, Ling J (2021) Attention-based label consistency for semi-supervised deep learning based image classification. Neurocomputing 453:731\u2013741","journal-title":"Neurocomputing"},{"key":"5688_CR35","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s11063-019-10132-7","volume":"51","author":"W Zhou","year":"2020","unstructured":"Zhou W et al (2020) Mutual improvement between temporal ensembling and virtual adversarial training. Neural Process Lett 51:1111\u20131124","journal-title":"Neural Process Lett"},{"key":"5688_CR36","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.ins.2021.07.059","volume":"578","author":"W Ding","year":"2021","unstructured":"Ding W, Abdel-Basset M, Hawash H (2021) RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions. Inf Sci 578:559\u2013573","journal-title":"Inf Sci"},{"key":"5688_CR37","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.neucom.2018.12.089","volume":"396","author":"C Feng","year":"2020","unstructured":"Feng C et al (2020) Domain adaptation with SBADA-GAN and Mean Teacher. Neurocomputing 396:577\u2013586","journal-title":"Neurocomputing"},{"key":"5688_CR38","doi-asserted-by":"crossref","first-page":"108140","DOI":"10.1016\/j.patcog.2021.108140","volume":"120","author":"L Liu","year":"2021","unstructured":"Liu L, Tan RT (2021) Certainty driven consistency loss on multi-teacher networks for semi-supervised learning. Pattern Recogn 120:108140","journal-title":"Pattern Recogn"},{"key":"5688_CR39","volume":"140","author":"M Yang","year":"2023","unstructured":"Yang M et al (2023) Discriminative semi-supervised learning via deep and dictionary representation for image classification. Pattern Recogn 140:109521","journal-title":"Pattern Recogn"},{"key":"5688_CR40","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neunet.2021.11.026","volume":"146","author":"E Tu","year":"2022","unstructured":"Tu E et al (2022) Deep semi-supervised learning via dynamic anchor graph embedding in latent space. Neural Netw 146:350\u2013360","journal-title":"Neural Netw"},{"issue":"15","key":"5688_CR41","doi-asserted-by":"crossref","first-page":"8856","DOI":"10.3390\/app13158856","volume":"13","author":"T Jiang","year":"2023","unstructured":"Jiang T et al (2023) Reliamatch: Semi-supervised classification with reliable match. Appl Sci 13(15):8856","journal-title":"Appl Sci"},{"key":"5688_CR42","doi-asserted-by":"crossref","first-page":"109032","DOI":"10.1016\/j.patcog.2022.109032","volume":"133","author":"X Huo","year":"2023","unstructured":"Huo X et al (2023) Collaborative learning with unreliability adaptation for semi-supervised image classification. Pattern Recogn 133:109032","journal-title":"Pattern Recogn"},{"key":"5688_CR43","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.neucom.2023.01.027","volume":"528","author":"A Gangwar","year":"2023","unstructured":"Gangwar A et al (2023) Triple-BigGAN: Semi-supervised generative adversarial networks for image synthesis and classification on sexual facial expression recognition. Neurocomputing 528:200\u2013216","journal-title":"Neurocomputing"},{"key":"5688_CR44","doi-asserted-by":"crossref","unstructured":"Chen L et al (2020) Seqvat: Virtual adversarial training for semi-supervised sequence labeling. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics","DOI":"10.18653\/v1\/2020.acl-main.777"},{"issue":"8","key":"5688_CR45","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2018","unstructured":"Miyato T et al (2018) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 41(8):1979\u20131993","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5688_CR46","doi-asserted-by":"crossref","unstructured":"Park S, Park J, Shin SJ, Moon IC (2018) Adversarial dropout for supervised and semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence 32(1)","DOI":"10.1609\/aaai.v32i1.11634"},{"key":"5688_CR47","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn K et al (2020) Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Adv Neural Inf Process Syst 33:596\u2013608","journal-title":"Adv Neural Inf Process Syst"},{"key":"5688_CR48","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.patrec.2022.09.020","volume":"164","author":"T Yamaguchi","year":"2022","unstructured":"Yamaguchi T, Murakawa M (2022) Mixup gamblers+: Learning interpolated pseudo \u201cuncertainty\u201d in latent feature space for reliable inference. Pattern Recogn Lett 164:191\u2013199","journal-title":"Pattern Recogn Lett"},{"key":"5688_CR49","doi-asserted-by":"crossref","first-page":"107024","DOI":"10.1016\/j.compbiomed.2023.107024","volume":"162","author":"B Jahanyar","year":"2023","unstructured":"Jahanyar B, Tabatabaee H, Rowhanimanesh A (2023) MS-ACGAN: A modified auxiliary classifier generative adversarial network for schizophrenia\u2019s samples augmentation based on microarray gene expression data. Comput Biol Med 162:107024","journal-title":"Comput Biol Med"},{"key":"5688_CR50","doi-asserted-by":"crossref","first-page":"103462","DOI":"10.1016\/j.cviu.2022.103462","volume":"221","author":"\u0141 Struski","year":"2022","unstructured":"Struski \u0141 et al (2022) Locogan\u2014locally convolutional gan. Comput Vis Image Underst 221:103462","journal-title":"Comput Vis Image Underst"},{"key":"5688_CR51","doi-asserted-by":"crossref","first-page":"110890","DOI":"10.1016\/j.asoc.2023.110890","volume":"148","author":"J Toutouh","year":"2023","unstructured":"Toutouh J et al (2023) Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classification. Appl Soft Comput 148:110890","journal-title":"Appl Soft Comput"},{"key":"5688_CR52","doi-asserted-by":"crossref","first-page":"108470","DOI":"10.1016\/j.compeleceng.2022.108470","volume":"106","author":"MA Contreras-Cruz","year":"2023","unstructured":"Contreras-Cruz MA et al (2023) Generative Adversarial Networks for anomaly detection in aerial images. Comput Electr Eng 106:108470","journal-title":"Comput Electr Eng"},{"key":"5688_CR53","doi-asserted-by":"crossref","first-page":"126629","DOI":"10.1016\/j.neucom.2023.126629","volume":"554","author":"C Huang","year":"2023","unstructured":"Huang C et al (2023) A review of deep learning in dentistry. Neurocomputing 554:126629","journal-title":"Neurocomputing"},{"key":"5688_CR54","doi-asserted-by":"crossref","first-page":"120854","DOI":"10.1016\/j.eswa.2023.120854","volume":"232","author":"Y Zhang","year":"2023","unstructured":"Zhang Y et al (2023) Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning. Expert Syst Appl 232:120854","journal-title":"Expert Syst Appl"},{"key":"5688_CR55","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neunet.2021.01.023","volume":"138","author":"D Peng","year":"2021","unstructured":"Peng D et al (2021) SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis. Neural Netw 138:57\u201367","journal-title":"Neural Netw"},{"issue":"13","key":"5688_CR56","doi-asserted-by":"crossref","first-page":"14665","DOI":"10.1007\/s10489-022-03541-0","volume":"52","author":"X Wu","year":"2022","unstructured":"Wu X et al (2022) Face aging with pixel-level alignment GAN. Appl Intell 52(13):14665\u201314678","journal-title":"Appl Intell"},{"key":"5688_CR57","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1016\/j.ins.2020.08.117","volume":"546","author":"W Xu","year":"2021","unstructured":"Xu W, Jiang L, Li C (2021) Improving data and model quality in crowdsourcing using cross-entropy-based noise correction. Inf Sci 546:803\u2013814","journal-title":"Inf Sci"},{"key":"5688_CR58","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.media.2019.07.004","volume":"57","author":"Y Xie","year":"2019","unstructured":"Xie Y, Zhang J, Xia Y (2019) Semi-supervised adversarial model for benign\u2013malignant lung nodule classification on chest CT. Med Image Anal 57:237\u2013248","journal-title":"Med Image Anal"},{"key":"5688_CR59","first-page":"3290","volume":"33","author":"S Garg","year":"2020","unstructured":"Garg S et al (2020) A unified view of label shift estimation. Adv Neural Inf Process Syst 33:3290\u20133300","journal-title":"Adv Neural Inf Process Syst"},{"key":"5688_CR60","doi-asserted-by":"crossref","first-page":"110022","DOI":"10.1016\/j.patcog.2023.110022","volume":"146","author":"X Huo","year":"2024","unstructured":"Huo X, Zhang Y, Wu S (2024) Semi-supervised class-conditional image synthesis with Semantics-guided Adaptive Feature Transforms. Pattern Recogn 146:110022","journal-title":"Pattern Recogn"},{"key":"5688_CR61","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.neucom.2021.03.059","volume":"449","author":"Z Qi","year":"2021","unstructured":"Qi Z et al (2021) Pccm-gan: Photographic text-to-image generation with pyramid contrastive consistency model. Neurocomputing 449:330\u2013341","journal-title":"Neurocomputing"},{"key":"5688_CR62","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning. NIPS workshop on deep learning and unsupervised feature learning 2011(2):4"},{"key":"5688_CR63","doi-asserted-by":"crossref","first-page":"102257","DOI":"10.1016\/j.bspc.2020.102257","volume":"64","author":"M Canayaz","year":"2021","unstructured":"Canayaz M (2021) MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images. Biomed Signal Process Control 64:102257","journal-title":"Biomed Signal Process Control"},{"key":"5688_CR64","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images"},{"key":"5688_CR65","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.ins.2019.06.064","volume":"507","author":"J Xu","year":"2020","unstructured":"Xu J, Zhang Y, Miao D (2020) Three-way confusion matrix for classification: A measure driven view. Inf Sci 507:772\u2013794","journal-title":"Inf Sci"},{"key":"5688_CR66","doi-asserted-by":"crossref","first-page":"103345","DOI":"10.1016\/j.compbiomed.2019.103345","volume":"111","author":"S Deepak","year":"2019","unstructured":"Deepak S, Ameer P (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345","journal-title":"Comput Biol Med"},{"key":"5688_CR67","unstructured":"Verma V et al (2019) Manifold mixup: Better representations by interpolating hidden states. in International conference on machine learning. PMLR"},{"key":"5688_CR68","doi-asserted-by":"crossref","first-page":"105837","DOI":"10.1016\/j.knosys.2020.105837","volume":"197","author":"J Chen","year":"2020","unstructured":"Chen J, Yang M, Gao G (2020) Semi-supervised dual-branch network for image classification. Knowl-Based Syst 197:105837","journal-title":"Knowl-Based Syst"},{"key":"5688_CR69","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.neucom.2021.12.093","volume":"493","author":"X Xia","year":"2022","unstructured":"Xia X et al (2022) GAN-based anomaly detection: A review. Neurocomputing 493:497\u2013535","journal-title":"Neurocomputing"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05688-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05688-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05688-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T13:29:26Z","timestamp":1723728566000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05688-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,6]]},"references-count":69,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["5688"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05688-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,8,6]]},"assertion":[{"value":"14 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 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":"This article does not contain any studies with human participants or animals performed by any of the authors. The datasets used in the manuscript are derived from publicly available data sets and may be obtained from the appropriate authors upon reasonable request.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent"}},{"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 interest"}}]}}