{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:12:40Z","timestamp":1740175960734,"version":"3.37.3"},"reference-count":25,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1109\/lra.2020.3012127","type":"journal-article","created":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T21:09:54Z","timestamp":1595884194000},"page":"6740-6747","source":"Crossref","is-referenced-by-count":2,"title":["Explicit Domain Adaptation With Loosely Coupled Samples"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3084-6675","authenticated-orcid":false,"given":"Oliver","family":"Scheel","sequence":"first","affiliation":[]},{"given":"Loren","family":"Schwarz","sequence":"additional","affiliation":[]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5598-5212","authenticated-orcid":false,"given":"Federico","family":"Tombari","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref11","article-title":"Learning linear transformations for fast arbitrary style transfer","author":"li","year":"0","journal-title":"Int Conf Comput Vis Pattern Recognit"},{"key":"ref12","first-page":"2017","article-title":"Spatial transformer networks","author":"jaderberg","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref13","first-page":"1134","article-title":"Learning with augmented features for heterogeneous domain adaptation","author":"duan","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","first-page":"11","article-title":"Linear supervised transfer learning for generalized matrix lvq","author":"paa\u00dfen","year":"0","journal-title":"Proc Workshop New Challenges Neural Comput"},{"key":"ref15","first-page":"2058","article-title":"Return of frustratingly easy domain adaptation","author":"sun","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1049\/cp:19991218"},{"article-title":"MNIST handwritten digit database","year":"2010","author":"lecun","key":"ref19"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.11"},{"key":"ref3","first-page":"11","article-title":"Linear supervised transfer learning for generalized matrix lvq","author":"paa\u00dfen","year":"0","journal-title":"Proc Workshop New Challenges Neural Comput"},{"key":"ref6","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"0","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.279"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.754"},{"key":"ref2","first-page":"9","article-title":"Unsupervised transfer learning for time series via self-predictive modelling-first results","author":"aswolinskiy","year":"0","journal-title":"Proc Workshop New Challenges Neural Comput"},{"key":"ref9","article-title":"Pedestrian-synthesis-gan: Generating pedestrian data in real scene and beyond","author":"ouyang","year":"2018","journal-title":"arXiv 1804 02047"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793648"},{"key":"ref22","first-page":"3118","article-title":"Brain4cars: Car that knows before you do via sensory-fusion deep learning architecture","author":"jain","year":"0","journal-title":"Proc Int Conf Robot Automat"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2014.6856491"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"year":"2006","key":"ref23","article-title":"Ngsim project"},{"key":"ref25","article-title":"Deep multi-scale video prediction beyond mean square error","author":"mathieu","year":"0","journal-title":"Proc Int Conf Learn Representations"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/9133350\/09149693.pdf?arnumber=9149693","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:33:57Z","timestamp":1651080837000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9149693\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10]]},"references-count":25,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/lra.2020.3012127","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"type":"electronic","value":"2377-3766"},{"type":"electronic","value":"2377-3774"}],"subject":[],"published":{"date-parts":[[2020,10]]}}}