{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:20:10Z","timestamp":1778048410399,"version":"3.51.4"},"reference-count":58,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,6]]},"DOI":"10.1109\/wacv61042.2026.00371","type":"proceedings-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T19:59:32Z","timestamp":1778011172000},"page":"3803-3812","source":"Crossref","is-referenced-by-count":0,"title":["High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization"],"prefix":"10.1109","author":[{"given":"Masih","family":"Aminbeidokhti","sequence":"first","affiliation":[{"name":"&#x00C9;cole de Technologie Sup&#x00E9;Rieure,Montreal,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heitor Rapela","family":"Medeiros","sequence":"additional","affiliation":[{"name":"&#x00C9;cole de Technologie Sup&#x00E9;Rieure,Montreal,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srikanth","family":"Muralidharan","sequence":"additional","affiliation":[{"name":"&#x00C9;cole de Technologie Sup&#x00E9;Rieure,Montreal,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Granger","sequence":"additional","affiliation":[{"name":"&#x00C9;cole de Technologie Sup&#x00E9;Rieure,Montreal,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Pedersoli","sequence":"additional","affiliation":[{"name":"&#x00C9;cole de Technologie Sup&#x00E9;Rieure,Montreal,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Generalizing to unseen domains via distribution matching","author":"Albuquerque","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00221"},{"key":"ref3","article-title":"Invariant risk minimization","author":"Arjovsky","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0601"},{"key":"ref5","first-page":"27","article-title":"Learning with pseudo-ensembles","author":"Bachman","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"ref7","first-page":"21189","article-title":"Exploiting domain-specific features to enhance domain generalization","volume":"34","author":"Bui","year":"2021","journal-title":"Neurips"},{"key":"ref8","first-page":"22405","article-title":"Swad: Domain generalization by seeking flat minima","volume":"34","author":"Cha","year":"2021","journal-title":"Neurips"},{"key":"ref9","first-page":"4623","article-title":"Explore and exploit the diverse knowledge in model zoo for domain generalization","volume-title":"ICML","author":"Chen","year":"2023"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379"},{"key":"ref13","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.208"},{"key":"ref15","article-title":"Deep ensembles: A loss landscape perspective","author":"Fort","year":"2019"},{"issue":"1","key":"ref16","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"JMLR"},{"key":"ref17","first-page":"31","article-title":"Dropblock: A regularization method for convolutional networks","author":"Ghiasi","year":"2018","journal-title":"Neurips"},{"key":"ref18","article-title":"Model patching: Closing the subgroup performance gap with data augmentation","author":"Goel","year":"2020"},{"key":"ref19","article-title":"In search of lost domain generalization","author":"Gulrajani","year":"2020"},{"key":"ref20","article-title":"Parameter-efficient fine-tuning for large models: A comprehensive survey","author":"Han","year":"2024"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref22","article-title":"Augmix: A simple data processing method to improve robustness and uncertainty","author":"Hendrycks","year":"2019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-35280-8_1432"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"ref25","article-title":"Averaging weights leads to wider optima and better generalization","author":"Izmailov","year":"2018"},{"key":"ref26","author":"Koh","year":"2021","journal-title":"Wilds: A benchmark of in-the-wild distribution shifts"},{"key":"ref27","author":"Koyama","year":"2020","journal-title":"Out-of-distribution generalization with maximal invariant predictor"},{"key":"ref28","article-title":"Fine-tuning can distort pretrained features and underperform out-of-distribution","author":"Kumar","year":"2022"},{"key":"ref29","first-page":"30","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"Lakshminarayanan","year":"2017","journal-title":"Neurips"},{"key":"ref30","article-title":"Fractalnet: Ultra-deep neural networks without residuals","author":"Larsson","year":"2016"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref32","article-title":"Mixout: Effective regularization to finetune large-scale pre-trained language models","author":"Lee","year":"2019"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref38","article-title":"Diverse weight averaging for out-of-distribution generalization","author":"Rame","year":"2022"},{"key":"ref39","author":"Rame","year":"2023","journal-title":"Model ratatouille: Recycling diverse models for out-of-distribution generalization"},{"key":"ref40","article-title":"Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization","author":"Sagawa","year":"2019"},{"key":"ref41","article-title":"Gradient matching for domain generalization","author":"Shi","year":"2021"},{"issue":"1","key":"ref42","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"JMLR"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref44","first-page":"24193","article-title":"Training neural networks with fixed sparse masks","volume":"34","author":"Sung","year":"2021","journal-title":"Neurips"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"ref46","article-title":"Principles of risk minimization for learning theory","volume":"4","author":"Vapnik","year":"1991","journal-title":"Neurips"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref48","first-page":"1058","article-title":"Regularization of neural networks using drop-connect","volume-title":"ICML","author":"Wan","year":"2013"},{"key":"ref49","first-page":"6514","article-title":"Hyperparameter ensembles for robustness and uncertainty quantification","volume":"33","author":"Wenzel","year":"2020","journal-title":"Neurips"},{"key":"ref50","first-page":"23965","article-title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","volume-title":"ICML","author":"Wortsman","year":"2022"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00780"},{"key":"ref52","first-page":"2825","article-title":"Explicit inductive bias for transfer learning with convolutional networks","volume-title":"ICML","author":"Xuhong","year":"2018"},{"key":"ref53","article-title":"Improve unsupervised domain adaptation with mixup training","author":"Yan","year":"2020"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref55","first-page":"40830","article-title":"Learning useful representations for shifting tasks and distributions","volume-title":"ICML","author":"Zhang","year":"2023"},{"key":"ref56","article-title":"Fine-tuning with very large dropout","author":"Zhang","year":"2024"},{"key":"ref57","article-title":"Domain generalization with mixstyle","author":"Zhou","year":"2021"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195549"}],"event":{"name":"2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","location":"Tucson, AZ, USA","start":{"date-parts":[[2026,3,6]]},"end":{"date-parts":[[2026,3,10]]}},"container-title":["2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11491838\/11491925\/11492741.pdf?arnumber=11492741","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:00:04Z","timestamp":1778047204000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11492741\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1109\/wacv61042.2026.00371","relation":{},"subject":[],"published":{"date-parts":[[2026,3,6]]}}}