{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:27:39Z","timestamp":1730266059281,"version":"3.28.0"},"reference-count":30,"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\/ijcnn52387.2021.9533492","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-7","source":"Crossref","is-referenced-by-count":0,"title":["Adversarially Smoothed Feature Alignment for Visual Domain Adaptation"],"prefix":"10.1109","author":[{"given":"Mohamed","family":"Azzam","sequence":"first","affiliation":[]},{"given":"Si","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Aurele Tohokantche","family":"Gnanha","sequence":"additional","affiliation":[]},{"given":"Hau-San","family":"Wong","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00621"},{"journal-title":"International Conference on Learning Representations","article-title":"mixup: Beyond empirical risk minimization","year":"0","author":"zhang","key":"ref10"},{"journal-title":"International Conference on Learning Representations","article-title":"How does mixup help with robustness and generalization?","year":"0","author":"zhang","key":"ref11"},{"journal-title":"ArXiv Preprint","article-title":"Learning transferable features with deep adaptation networks","year":"2015","author":"long","key":"ref12"},{"key":"ref13","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"long","year":"0","journal-title":"Int Conference on Machine Learning"},{"key":"ref14","first-page":"2110","article-title":"Learning transferrable representations for unsupervised domain adaptation","author":"sener","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref15","first-page":"1640","article-title":"Conditional adversarial domain adaptation","author":"long","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref16","first-page":"4013","article-title":"Transferable adversarial training: A general approach to adapting deep classifiers","author":"liu","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00913"},{"journal-title":"International Conference on Learning Representations","article-title":"Mixup inference: Better exploiting mixup to defend adversarial attacks","year":"0","author":"pang","key":"ref19"},{"journal-title":"ArXiv Preprint","article-title":"Multi-source domain adaptation in the deep learning era: A systematic survey","year":"2020","author":"zhao","key":"ref28"},{"key":"ref4","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2963389"},{"key":"ref3","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"},{"journal-title":"ArXiv Preprint","article-title":"A dirt-t approach to unsupervised domain adaptation","year":"2018","author":"shu","key":"ref6"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00382"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00150"},{"key":"ref7","first-page":"9345","article-title":"Co-regularized alignment for unsupervised domain adaptation","author":"kumar","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"journal-title":"Proceedings of the Asian Conference on Computer Vision (ACCV)","article-title":"Contrastively smoothed class alignment for unsupervised domain adaptation","year":"0","author":"dai","key":"ref9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"journal-title":"ArXiv Preprint","article-title":"Virtual mixup training for unsupervised domain adaptation","year":"2019","author":"mao","key":"ref20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6123"},{"journal-title":"ArXiv Preprint","article-title":"Adam: A method for stochastic optimization","year":"2014","author":"kingma","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00271"},{"key":"ref26","first-page":"740","article-title":"Microsoft coco: Common objects in context","author":"lin","year":"0","journal-title":"European Conference on Computer Vision"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00887"}],"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\/09533492.pdf?arnumber=9533492","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:46:10Z","timestamp":1652197570000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533492\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533492","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}