{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T08:30:34Z","timestamp":1765701034258,"version":"3.37.3"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Landesgraduiertenf\u00f6rderungsgesetz (LGFG) Scholarship through Ulm University"},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["SCHW 623\/7-1"],"award-info":[{"award-number":["SCHW 623\/7-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3325284","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T18:04:52Z","timestamp":1697565892000},"page":"117368-117384","source":"Crossref","is-referenced-by-count":1,"title":["Class-Variational Learning With Capsule Networks for Deep Entity-Subspace Clustering"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8182-6781","authenticated-orcid":false,"given":"Nikolai A. K.","family":"Steur","sequence":"first","affiliation":[{"name":"Institute of Neural Information Processing, Ulm University, Ulm, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5118-0812","authenticated-orcid":false,"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[{"name":"Institute of Neural Information Processing, Ulm University, Ulm, Germany"}]}],"member":"263","reference":[{"key":"ref13","first-page":"1","article-title":"Dynamic routing between capsules","author":"sabour","year":"2017","journal-title":"Proc 31st Conf Neural Inf Process Syst (NIPS)"},{"key":"ref12","first-page":"1","article-title":"Unsupervised deep embedding for clustering analysis","author":"xie","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn (ICML)"},{"key":"ref15","article-title":"Sparse unsupervised capsules generalize better","author":"rawlinson","year":"2018","journal-title":"arXiv 1804 06094"},{"key":"ref14","first-page":"1","article-title":"Matrix capsules with em routing","author":"hinton","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref53","first-page":"1","article-title":"Co-mixup: Saliency guided joint mixup with supermodular diversity","author":"kim","year":"2021","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref52","first-page":"5275","article-title":"Puzzle mix: Exploiting saliency and local statistics for optimal mixup","author":"kim","year":"2020","journal-title":"Proc 37th Int Conf Mach Learn (ICML)"},{"key":"ref11","article-title":"From clustering to cluster explanations via neural networks","author":"kauffmann","year":"2021","journal-title":"arXiv 1906 07632"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3163362"},{"journal-title":"The MNIST Database of Handwritten Digits","year":"2010","author":"lecun","key":"ref17"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref19","first-page":"1","article-title":"Stochastic neighbor embedding","author":"hinton","year":"2002","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref18","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref50","first-page":"1","article-title":"Towards understanding the data dependency of mixup-style training","author":"chidambaram","year":"2022","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref46","first-page":"1","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"ref47","first-page":"1","article-title":"Vicinal risk minimization","volume":"13","author":"chapelle","year":"2000","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1080\/03610927408827101"},{"key":"ref41","first-page":"410","article-title":"V-measure: A conditional entropy-based external cluster evaluation measure","author":"rosenberg","year":"2007","journal-title":"Proc Joint Conf Empirical Methods Natural Lang Process Comput Natural Lang Learn (EMNLP-CoNLL)"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.2307\/2284239"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"ref49","first-page":"1","article-title":"How does mixup help with robustness and generalization?","author":"zhang","year":"2021","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref8","article-title":"Learning a task-specific deep architecture for clustering","author":"wang","year":"2015","journal-title":"arXiv 1509 00151v3"},{"key":"ref7","first-page":"4728","article-title":"Multi-modal deep clustering: Unsupervised partitioning of images","author":"shiran","year":"2020","journal-title":"Proc Int Conf Pattern Recognit (ICPR)"},{"key":"ref9","first-page":"1","article-title":"Adversarial learning for robust deep clustering","author":"yang","year":"2020","journal-title":"Proc 34th Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref4","first-page":"3861","article-title":"Towards K-means-friendly spaces: Simultaneous deep learning and clustering","volume":"70","author":"yang","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn (ICML)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.612"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00419"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"ref35","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref34","article-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016","journal-title":"arXiv 1603 04467v2"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2049235"},{"key":"ref36","first-page":"1","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref31","first-page":"3110","article-title":"Investigating capsule networks with dynamic routing for text classification","author":"zhao","year":"2018","journal-title":"Proc Conf Empirical Methods Natural Lang Process (EMNLP)"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3110911"},{"journal-title":"TensorFlow Datasets a Collection of Ready-to-Use Datasets","year":"2023","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.320"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70096-0_39"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7471631"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"ref38","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"5","author":"macqueen","year":"1967","journal-title":"Proc 5th Berkeley Symp Math Statist Probab"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.556"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_16"},{"key":"ref26","first-page":"1","article-title":"Stacked capsule autoencoders","author":"kosiorek","year":"2019","journal-title":"Proc 33rd Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"ref20","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8916"},{"key":"ref28","first-page":"1","article-title":"Unsupervised feature extraction by time-contrastive learning and nonlinear ICA","author":"hyv\u00e4rinen","year":"2016","journal-title":"Proc 30th Conf Neural Inf Process Syst (NIPS)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414643"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_6"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10286846.pdf?arnumber=10286846","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T20:06:11Z","timestamp":1701115571000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10286846\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3325284","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2023]]}}}