{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:25:53Z","timestamp":1730265953726,"version":"3.28.0"},"reference-count":36,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"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":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533304","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Joint Distribution Adaptation via Wasserstein Adversarial Training"],"prefix":"10.1109","author":[{"given":"Xiaolu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huikang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s00440-006-0004-7","article-title":"Quantitative concentration inequalities for empirical measures on non-compact spaces","volume":"137","author":"bolley","year":"2007","journal-title":"Probability Theory and Related Fields"},{"key":"ref35","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"van der maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref34","first-page":"443","article-title":"Deep coral: Correlation alignment for deep domain adaptation","author":"sun","year":"0","journal-title":"European Conference on Computer Vision"},{"key":"ref10","first-page":"5767","article-title":"Improved training of wasserstein gans","author":"gulrajani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1561\/2200000073"},{"key":"ref12","first-page":"3730","article-title":"Joint distribution optimal transportation for domain adaptation","author":"courty","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_28"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.547"},{"key":"ref16","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"long","year":"0","journal-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70"},{"key":"ref17","first-page":"137","article-title":"Analysis of representations for domain adaptation","author":"shai","year":"2007","journal-title":"Advances in neural information processing systems"},{"key":"ref18","volume":"338","author":"villani","year":"2008","journal-title":"Optimal Transport Old and New"},{"journal-title":"Lecture 6 5 - rmsprop coursera Neural networks for machine learning","year":"2012","author":"tieleman","key":"ref19"},{"key":"ref28","first-page":"440","article-title":"Biographies, bol-lywood, boom-boxes and blenders: Domain adaptation for sentiment classification","author":"blitzer","year":"0","journal-title":"Proceedings annual meeting of the Association for Computational Linguistics"},{"key":"ref4","article-title":"A review on generative adversarial networks: Algorithms, theory, and applications","author":"gui","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref27","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref6","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"The Journal of Machine Learning Research"},{"key":"ref29","first-page":"1627","article-title":"Marginalized denoising autoencoders for domain adaptation","author":"minmin","year":"0","journal-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning"},{"key":"ref5","first-page":"2672","article-title":"Gen-erative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref8","article-title":"Wasserstein distance guided representation learning for domain adaptation","author":"shen","year":"0","journal-title":"Thirty-Second AAAI Conference on Artificial Intelligence"},{"key":"ref7","first-page":"7167","article-title":"Adver-sarial discriminative domain adaptation","author":"tzeng","year":"0","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2615921"},{"key":"ref9","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref1","article-title":"Visual domain adaptation: An overview of recent advances","volume":"2","author":"patel","year":"2014","journal-title":"IEEE Signal Processing Magazine"},{"key":"ref20","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s10472-013-9371-9","article-title":"Domain adaptation-can quantity compensate for quality?","volume":"70","author":"shai","year":"2014","journal-title":"Annals of Mathematics and Artificial Intelligence"},{"key":"ref21","first-page":"641","article-title":"Access to unlabeled data can speed up prediction time","author":"urner","year":"0","journal-title":"Proceedings of the 28th International Conference on International Conference on Machine Learning"},{"key":"ref24","first-page":"213","article-title":"Adapting visual category models to new domains","author":"saenko","year":"0","journal-title":"European Conference on Computer Vision"},{"key":"ref23","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"gong","year":"0","journal-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref26","first-page":"647","article-title":"Decaf: A deep convolutional activation feature for generic visual recognition","author":"donahue","year":"0","journal-title":"International Conference on Machine Learning"},{"journal-title":"Caltech-256 Object Category Dataset","year":"2007","author":"griffin","key":"ref25"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533304.pdf?arnumber=9533304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:46:16Z","timestamp":1652197576000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533304\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533304","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}