{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:53:00Z","timestamp":1776783180518,"version":"3.51.2"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,12]],"date-time":"2021-12-12T00:00:00Z","timestamp":1639267200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,12]],"date-time":"2021-12-12T00:00:00Z","timestamp":1639267200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003170","name":"Stiftelsen f\u00f6r Kunskaps- och Kompetensutveckling","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003170","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,12]]},"DOI":"10.1109\/gcaiot53516.2021.9693050","type":"proceedings-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T22:29:35Z","timestamp":1643668175000},"page":"45-50","source":"Crossref","is-referenced-by-count":3,"title":["A weakly-supervised deep domain adaptation method for multi-modal sensor data"],"prefix":"10.1109","author":[{"given":"Radu-Casian","family":"Mihailescu","sequence":"first","affiliation":[{"name":"Malm&#x00F6; University, Internet of Things and People Research Center,Department of Computer Science,20506, Malm&#x00F6;,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref30","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"yosinski","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref10","first-page":"2137","article-title":"Discriminative learning under covariate shift","volume":"10","author":"bickel","year":"2009","journal-title":"Journal of Machine Learning Research"},{"key":"ref11","article-title":"Domain adaptation via pseudo in-domain data selection","author":"axelrod","year":"0"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.03.020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.10.061"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref16","article-title":"Domain separation networks","volume":"29","author":"bousmalis","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref17","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","author":"ganin","year":"2015","journal-title":"Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37 ser ICML&#x2019;15"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1109\/ICCV.2017.244","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"zhu","year":"2017","journal-title":"2017 IEEE International Conference on Computer Vision (ICCV)"},{"key":"ref19","first-page":"1857","article-title":"Learning to discover cross-domain relations with generative adversarial networks","volume":"70","author":"kim","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2010.112"},{"key":"ref27","first-page":"1627","article-title":"Marginalized denoising autoencoders for domain adaptation","author":"chen","year":"2012","journal-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning ser ICML&#x2019;12"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v28i1.8931","article-title":"Instance-based domain adaptation in NLP via in-target-domain logistic approximation","volume":"28","author":"xia","year":"2014","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref29","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2014","journal-title":"2nd International Conference on Learning Representations ICLR 2014 Banff AB Canada April 14&#x2013;16 2014 Conference Track Proceedings"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref8","first-page":"222","article-title":"Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation","volume":"28","author":"gong","year":"2013","journal-title":"Proceedings of the 30th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9010180"},{"key":"ref9","article-title":"Direct importance estimation with model selection and its application to covariate shift adaptation","volume":"20","author":"sugiyama","year":"2008","journal-title":"Advances in neural information processing systems"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/fi12080133"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298629"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00525"},{"key":"ref23","article-title":"Lsda: Large scale detection through adaptation","volume":"27","author":"hoffman","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref26","first-page":"513","article-title":"Domain adaptation for large-scale sentiment classification: A deep learning approach","author":"glorot","year":"2011","journal-title":"Proceedings of the 28th International Conference on International Conference on Machine Learning ser ICML&#x2019;11"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.630"}],"event":{"name":"2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)","location":"Dubai, United Arab Emirates","start":{"date-parts":[[2021,12,12]]},"end":{"date-parts":[[2021,12,16]]}},"container-title":["2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9692905\/9691504\/09693050.pdf?arnumber=9693050","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T10:33:56Z","timestamp":1674642836000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9693050\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,12]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/gcaiot53516.2021.9693050","relation":{},"subject":[],"published":{"date-parts":[[2021,12,12]]}}}