{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:14:43Z","timestamp":1762377283768,"version":"3.28.0"},"reference-count":61,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"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":[[2022,8,21]]},"DOI":"10.1109\/icpr56361.2022.9956338","type":"proceedings-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T19:34:13Z","timestamp":1669750453000},"page":"2149-2156","source":"Crossref","is-referenced-by-count":1,"title":["Domain Generalization via Selective Consistency Regularization for Time Series Classification"],"prefix":"10.1109","author":[{"given":"Wenyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute for Infocomm Research, A*STAR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Ragab","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*STAR,Centre for Frontier AI Research, A*STAR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan-Sheng","family":"Foo","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*STAR,Centre for Frontier AI Research, A*STAR"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Reducing domain gap via style-agnostic networks","year":"2019","author":"nam","key":"ref39"},{"key":"ref38","article-title":"Learning to generate novel domains for domain generalization","author":"zhou","year":"2020","journal-title":"ECCV"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00714"},{"key":"ref32","article-title":"Generalizing across domains via cross-gradient training","author":"shankar","year":"2018","journal-title":"ICLRE"},{"key":"ref31","article-title":"Generalizing to unseen domains via adversarial data augmentation","author":"volpi","year":"2018","journal-title":"NeurIPS"},{"key":"ref30","first-page":"187","article-title":"Domain generalization with domain-specific aggregation modules","author":"d\u2019innocente","year":"2019","journal-title":"Pattern Recognition"},{"key":"ref37","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"NIPS"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2921336"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053273"},{"article-title":"Improve unsupervised domain adaptation with mixup training","year":"2020","author":"yan","key":"ref34"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16846"},{"key":"ref61","article-title":"Adversarial multiple source domain adaptation","author":"zhao","year":"2018","journal-title":"NeurIPS"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00948"},{"key":"ref27","article-title":"Systematic generalisation with group invariant predictions","author":"ahmed","year":"2021","journal-title":"ICLRE"},{"key":"ref29","article-title":"Matching networks for one shot learning","author":"vinyals","year":"2016","journal-title":"NeurIPS"},{"article-title":"The many faces of robustness: A critical analysis of out-of-distribution generalization","year":"2020","author":"hendrycks","key":"ref2"},{"key":"ref1","article-title":"In Search of Lost Domain Generalization","author":"gulrajani","year":"2021","journal-title":"ICLRE"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01155"},{"key":"ref22","article-title":"Deep coral: Correlation alignment for deep domain adaptation","author":"sun","year":"2016","journal-title":"ECCV Workshops"},{"key":"ref21","article-title":"Domain separation networks","author":"bousmalis","year":"2016","journal-title":"NIPS"},{"key":"ref24","article-title":"Domain generalization via model-agnostic learning of semantic features","author":"dou","year":"2019","journal-title":"NeurIPS"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref26","article-title":"Respecting domain relations: Hypothesis invariance for domain generalization","author":"wang","year":"2020","journal-title":"ICPR"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref50","first-page":"1","article-title":"Domain generalization by marginal transfer learning","volume":"22","author":"blanchard","year":"2021","journal-title":"Journal of Machine Learning Research"},{"key":"ref51","article-title":"Deep domain generalization via conditional invariant adversarial networks","author":"li","year":"2018","journal-title":"ECCV"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref58","article-title":"MIMIC-III clinical database","author":"johnson","year":"2016","journal-title":"PhysioNet"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413872"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2015.199"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.3390\/s17020425"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.04.021"},{"key":"ref52","article-title":"On calibration of modern neural networks","author":"guo","year":"2017","journal-title":"ICML"},{"key":"ref10","first-page":"59:1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1498"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00258"},{"key":"ref12","article-title":"MetaReg: Towards domain generalization using meta-regularization","author":"balaji","year":"2018","journal-title":"NeurIPS"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403228"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2898619"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref16","article-title":"Domain generalization using a mixture of multiple latent domains","author":"m","year":"2020","journal-title":"AAAI"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref18","article-title":"Conditional adversarial domain adaptation","author":"long","year":"2018","journal-title":"NeurIPS"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/628"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9340858"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00153"},{"key":"ref5","article-title":"Robot learning in homes: Improving generalization and reducing dataset bias","author":"gupta","year":"2018","journal-title":"NIPS"},{"article-title":"Frustratingly simple domain generalization via image stylization","year":"2020","author":"somavarapu","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451318"},{"key":"ref49","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"gretton","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00233"},{"key":"ref46","article-title":"Self-challenging improves cross-domain generalization","author":"huang","year":"2020","journal-title":"ECCV"},{"article-title":"Predicting with high correlation features","year":"2019","author":"arpit","key":"ref45"},{"article-title":"Out-of-distribution generalization via risk extrapolation (REx)","year":"2020","author":"krueger","key":"ref48"},{"article-title":"Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization","year":"2019","author":"sagawa","key":"ref47"},{"article-title":"The risks of invariant risk minimization","year":"2020","author":"rosenfeld","key":"ref42"},{"article-title":"Invariant risk minimization","year":"2019","author":"arjovsky","key":"ref41"},{"key":"ref44","article-title":"Domain generalization using causal matching","author":"mahajan","year":"2020","journal-title":"Uncertainty and Robustness in Deep Learning (ICML Workshop)"},{"article-title":"Generalizing to unseen domains via distribution matching","year":"2020","author":"albuquerque","key":"ref43"}],"event":{"name":"2022 26th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2022,8,21]]},"location":"Montreal, QC, Canada","end":{"date-parts":[[2022,8,25]]}},"container-title":["2022 26th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9956007\/9955631\/09956338.pdf?arnumber=9956338","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T20:05:21Z","timestamp":1675109121000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9956338\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/icpr56361.2022.9956338","relation":{},"subject":[],"published":{"date-parts":[[2022,8,21]]}}}