{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:56:07Z","timestamp":1753887367306,"version":"3.41.2"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":297,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2019C003"],"award-info":[{"award-number":["LH2019C003"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","award":["2572018BH07"],"award-info":[{"award-number":["2572018BH07"]}],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Active learning aims to select the most valuable unlabelled samples for annotation. In this paper, we propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which selects samples based on their distribution, uncertainty, and redundancy. RRAAL includes a representation generator, state discriminator, and redundancy removal module (RRM). The purpose of the representation generator is to learn the feature representation of a sample, and the state discriminator predicts the state of the feature vector after concatenation. We added a sample discriminator to the representation generator to improve the representation learning ability of the generator and designed a norm online uncertainty indicator (Norm\u2010OUI) to provide a more accurate uncertainty score for the state discriminator. In addition, we designed an RRM based on a greedy algorithm to reduce the number of redundant samples in the labelled pool. The experimental results on four datasets show that the state discriminator, Norm\u2010OUI, and RRM can improve the performance of RRAAL, and RRAAL outperforms the previous state\u2010of\u2010the\u2010art active learning methods.<\/jats:p>","DOI":"10.1155\/2021\/4752568","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T04:20:09Z","timestamp":1635222009000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8692-6255","authenticated-orcid":false,"given":"Jifeng","family":"Guo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0940-3351","authenticated-orcid":false,"given":"Zhiqi","family":"Pang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4529-1975","authenticated-orcid":false,"given":"Wenbo","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Shi","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6883-0922","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2018.2792050"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.116.061102"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2933334"},{"key":"e_1_2_9_4_2","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","volume":"1","author":"Kingma D. P.","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_5_2","unstructured":"OualiY. HudelotC. andTamiM. An Overview of Deep Semi-supervised Learning 1 Proceedings of the International Conference on Machine Learning July 2020."},{"key":"e_1_2_9_6_2","doi-asserted-by":"crossref","unstructured":"DoulamisN.andDoulamisA. Semi-supervised deep learning for object tracking and classification Proceedings of the IEEE International Conference on Image Processing (ICIP) October 2014 Paris France 848\u2013852 https:\/\/doi.org\/10.1109\/icip.2014.7025170 2-s2.0-84949928730.","DOI":"10.1109\/ICIP.2014.7025170"},{"key":"e_1_2_9_7_2","unstructured":"LeeD. H. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks 3 Proceedings of the 2013 ICML Workshop on challenges in representation learning June 2013 Atlanta USA no. 2 896."},{"key":"e_1_2_9_8_2","unstructured":"NiuG. JitkrittumW. DaiB. HachiyaH. andSugiyamaM. Squared-loss mutual information regularization: a novel information-theoretic approach to semi-supervised learning Proceedings of the 2013 International Conference on Machine Learning June 2013 Atlanta USA 10\u201318."},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2016.2535367"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13030371"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"ZhaiX. OliverA. KolesnikovA. andBeyerL. S4l: self-supervised semi-supervised learning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) November 2019 Seoul Korea 1476\u20131485 https:\/\/doi.org\/10.1109\/iccv.2019.00156.","DOI":"10.1109\/ICCV.2019.00156"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/3287589"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-02121-4"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"BeluchW. H. GeneweinT. N\u00fcrnbergerA. andK\u00f6hlerJ. M. The power of ensembles for active learning in image classification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2018 Salt Lake City UT USA 9368\u20139377 https:\/\/doi.org\/10.1109\/cvpr.2018.00976 2-s2.0-85061664748.","DOI":"10.1109\/CVPR.2018.00976"},{"key":"e_1_2_9_16_2","unstructured":"GalY. IslamR. andGhahramaniZ. Deep bayesian active learning with image data Proceedings of the International Conference on Machine Learning August 2017 Sydney Australia 1183\u20131192."},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"SunY. ZhangJ. andZhangY. Multi-sensor image classification based on active learning Proceedings of the 2012 5th International Congress on Image and Signal Processing October 2012 Chongqing Sichuan China 1290\u20131293 https:\/\/doi.org\/10.1109\/cisp.2012.6469725 2-s2.0-84875008509.","DOI":"10.1109\/CISP.2012.6469725"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2008.2010404"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"JainS. D.andGraumanK. Active image segmentation propagation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA 2864\u20132873 https:\/\/doi.org\/10.1109\/cvpr.2016.313 2-s2.0-84986333999.","DOI":"10.1109\/CVPR.2016.313"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"VezhnevetsA. BuhmannJ. M. andFerrariV. Active learning for semantic segmentation with expected change Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2012 Providence RI USA 3162\u20133169 https:\/\/doi.org\/10.1109\/cvpr.2012.6248050 2-s2.0-84866706762.","DOI":"10.1109\/CVPR.2012.6248050"},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"ZhangB. LiL. YangS. WangS. ZhaZ. J. andHuangQ. State-relabeling adversarial active learning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2020 Seattle WA USA 8756\u20138765 https:\/\/doi.org\/10.1109\/cvpr42600.2020.00878.","DOI":"10.1109\/CVPR42600.2020.00878"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","unstructured":"SinhaS. EbrahimiS. andDarrellT. Variational adversarial active learning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) November 2019 Seoul Korea 5972\u20135981 https:\/\/doi.org\/10.1109\/iccv.2019.00607.","DOI":"10.1109\/ICCV.2019.00607"},{"key":"e_1_2_9_23_2","unstructured":"GoodfellowI. Pouget-AbadieJ. MirzaM. XuB. Warde-FarleyD. OzairS. CourvilleA. andBengioY. Generative adversarial nets Proceedings of the International Conference on Neural Information Processing Systems (NIPS) December 2014 Montreal Canada 2672\u20132680."},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6309596"},{"key":"e_1_2_9_25_2","unstructured":"ZhuJ. J.andBentoJ. Generative adversarial active learning Proceedings of the International Conference on Machine Learning August 2017 Sydney Australia."},{"key":"e_1_2_9_26_2","unstructured":"TranT. DoT. T. ReidI. andCarneiroG. Bayesian generative active deep learning Proceedings of the International Conference on Machine Learning June 2019 Long Beach CA USA."},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2868649"},{"key":"e_1_2_9_28_2","article-title":"Deep active learning over the long tail","volume":"1","author":"Geifman Y.","year":"2017","journal-title":"Computer Science Bibliography"},{"key":"e_1_2_9_29_2","doi-asserted-by":"crossref","unstructured":"JoshiA. J. PorikliF. andPapanikolopoulosN. Multi-class active learning for image classification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2009 Miami FL USA 2372\u20132379 https:\/\/doi.org\/10.1109\/cvpr.2009.5206627.","DOI":"10.1109\/CVPRW.2009.5206627"},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"KapoorA. GraumanK. UrtasunR. andDarrellT. Active learning with Gaussian processes for object categorization Proceedings of the IEEE Conference on International Conference on Computer Vision October 2007 Rio de Janeiro Brazil 1\u20138 https:\/\/doi.org\/10.1109\/iccv.2007.4408844 2-s2.0-50649102302.","DOI":"10.1109\/ICCV.2007.4408844"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2512104"},{"key":"e_1_2_9_32_2","unstructured":"BrinkerK. Incorporating diversity in active learning with support vector machines Proceedings of the International Conference on Machine Learning (ICML) August 2003 Washington DC USA 59\u201366."},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2700408"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2016.2636146"},{"key":"e_1_2_9_35_2","doi-asserted-by":"crossref","unstructured":"KaselimiM. DoulamisN. DoulamisA. VoulodimosA. andProtopapadakisE. Bayesian-optimized bidirectional LSTM regression model for non-intrusive load monitoring Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) May 2019 Brighton UK 2747\u20132751 https:\/\/doi.org\/10.1109\/icassp.2019.8683110 2-s2.0-85068991321.","DOI":"10.1109\/ICASSP.2019.8683110"},{"key":"e_1_2_9_36_2","doi-asserted-by":"crossref","unstructured":"YooD.andKweonI. S. Learning Loss for Active Learning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2019 Convention center in Long Beach CA USA 93\u2013102.","DOI":"10.1109\/CVPR.2019.00018"},{"key":"e_1_2_9_37_2","unstructured":"SenerO.andSavareseS. Active learning for convolutional neural networks: a core-set approach Proceedings of the 2017 Conference on Computer Vision and Pattern Recognition July 2017 Honolulu HI USA."},{"key":"e_1_2_9_38_2","unstructured":"KingmaD. P.andWellingM. Auto-encoding variational bayes Proceedings of the 2017 Conference on Machine Learning June 2013 Atlanta USA."},{"key":"e_1_2_9_39_2","unstructured":"MirzaM.andOsinderoS. Conditional generative adversarial nets Proceedings of the 2014 Conference on Machine Learning June 2014 Beijing China."},{"key":"e_1_2_9_40_2","unstructured":"TranT. PhamT. CarneiroG. PalmerL. andReidI. A bayesian data augmentation approach for learning deep models Proceedings of the International Conference on Neural Information Processing Systems (NIPS) December 2017 Long Beach CA USA 2797\u20132806."},{"key":"e_1_2_9_41_2","unstructured":"GalY. IslamR. andGhahramaniZ. Deep bayesian active learning with image data Proceedings of the International Conference on Machine Learning August 2017 Sydney Australia 1183\u20131192."},{"key":"e_1_2_9_42_2","unstructured":"MottaghiA.andYeungS. Adversarial representation active learning Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition June 2019 Long Beach CA USA."},{"volume-title":"Learning Multiple Layers of Features from Tiny Images","year":"2009","author":"Krizhevsky A.","key":"e_1_2_9_43_2"},{"key":"e_1_2_9_44_2","unstructured":"Fei-FeiL. FergusR. andPeronaP. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) June 2004 Washington DC. USA."},{"key":"e_1_2_9_45_2","doi-asserted-by":"crossref","unstructured":"CordtsM. OmranM. RamosS. RehfeldT. EnzweilerM. BenensonR. FrankeU. RothS. andSchieleB. The cityscapes dataset for semantic urban scene understanding Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Las Vegas Nevada USA 3213\u20133223 https:\/\/doi.org\/10.1109\/cvpr.2016.350 2-s2.0-84986255616.","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_2_9_46_2","doi-asserted-by":"crossref","unstructured":"JungH. ChoiM. K. JungJ. LeeJ.-H. KwonS. andJungW. Y. ResNet-based vehicle classification and localization in traffic surveillance systems Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) July 2017 Honolulu HI USA 61\u201367 https:\/\/doi.org\/10.1109\/cvprw.2017.129 2-s2.0-85030237719.","DOI":"10.1109\/CVPRW.2017.129"},{"key":"e_1_2_9_47_2","doi-asserted-by":"crossref","unstructured":"YuF. KoltunV. andFunkhouserT. Dilated residual networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2017 Honolulu HI USA 472\u2013480 https:\/\/doi.org\/10.1109\/cvpr.2017.75 2-s2.0-85042543570.","DOI":"10.1109\/CVPR.2017.75"},{"key":"e_1_2_9_48_2","unstructured":"GalY.andGhahramaniZ. Dropout as a bayesian approximation: representing model uncertainty in deep learning Proceedings of the International Conference on Machine Learning June 2016 New York NY USA 1050\u20131059."},{"key":"e_1_2_9_49_2","doi-asserted-by":"crossref","unstructured":"KuoW. H\u00e4neC. YuhE. MukherjeeP. andMalikJ. Cost-sensitive active learning for intracranial hemorrhage detection Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention September 2018 Granada Spain 715\u2013723 https:\/\/doi.org\/10.1007\/978-3-030-00931-1_82 2-s2.0-85053881690.","DOI":"10.1007\/978-3-030-00931-1_82"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4752568.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/4752568.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/4752568","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:04:00Z","timestamp":1722942240000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/4752568"}},"subtitle":[],"editor":[{"given":"Anastasios D.","family":"Doulamis","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/4752568"],"URL":"https:\/\/doi.org\/10.1155\/2021\/4752568","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-04-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4752568"}}