{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:37:03Z","timestamp":1743028623642,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030306441"},{"type":"electronic","value":"9783030306458"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30645-8_21","type":"book-chapter","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T08:08:15Z","timestamp":1567584495000},"page":"225-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised Domain Adaptation Using Full-Feature Whitening and Colouring"],"prefix":"10.1007","author":[{"given":"Subhankar","family":"Roy","sequence":"first","affiliation":[]},{"given":"Aliaksandr","family":"Siarohin","sequence":"additional","affiliation":[]},{"given":"Nicu","family":"Sebe","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,2]]},"reference":[{"key":"21_CR1","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Zen, G., Sangineto, E., Ricci, E., Sebe, N.: Unsupervised domain adaptation for personalized facial emotion recognition. In: ICMI (2014)","DOI":"10.1145\/2663204.2663247"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., Saenko, K.: Return of frustratingly easy domain adaptation. In: AAAI (2016)","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Saha, S., Banerjee, B., Merchant, S.N.: Unsupervised domain adaptation without source domain training samples: a maximum margin clustering based approach. In: ICVGIP (2016)","DOI":"10.1145\/3009977.3010033"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Roy, S., Siarohin, A., Sangineto, E., Bul\u00f2, S.R., Sebe, N., Ricci, E.: Unsupervised domain adaptation using feature-whitening and consensus loss. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00970"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Cariucci, F.M., Porzi, L., Caputo, B., Ricci, E., Bul\u00f2, S.R.: AutoDIAL: automatic domain alignment layers. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.542"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Russo, P., Carlucci, F.M., Tommasi, T., Caputo, B.: From source to target and back: symmetric bi-directional adaptive GAN. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00845"},{"key":"21_CR11","unstructured":"Hoffman, J., et al.: CyCADA: cycle-consistent adversarial domain adaptation. In: ICML (2018)"},{"key":"21_CR12","unstructured":"Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: NIPS (2016)"},{"key":"21_CR13","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: ICML (2015)"},{"key":"21_CR14","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)"},{"key":"21_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Mancini, M., Bul\u00f2, S.R., Caputo, B., Ricci, E.: AdaGraph: unifying predictive and continuous domain adaptation through graphs. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00673"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Mancini, M., Porzi, L., Rota Bul\u00f2, S., Caputo, B., Ricci, E.: Boosting domain adaptation by discovering latent domains. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00397"},{"key":"21_CR18","unstructured":"Siarohin, A., Sangineto, E., Sebe, N.: Whitening and coloring batch transform for GANs. In: ICLR (2019)"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Yao, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: CVPR (2010)","DOI":"10.1109\/CVPR.2010.5539857"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Xu, R., Chen, Z., Zuo, W., Yan, J., Lin, L.: Deep cocktail network: Multi-source unsupervised domain adaptation with category shift. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00417"},{"key":"21_CR21","unstructured":"Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation with multiple sources. In: NIPS (2009)"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. arXiv preprint arXiv:1812.01754 (2018)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"21_CR23","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)"},{"key":"21_CR24","unstructured":"French, G., Mackiewicz, M., Fisher, M.: Self-ensembling for visual domain adaptation. In: ICLR (2018)"},{"key":"21_CR25","unstructured":"Morerio, P., Cavazza, J., Murino, V.: Minimal-entropy correlation alignment for unsupervised deep domain adaptation. In: ICLR (2017, 2018)"},{"key":"21_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-030-01270-0_36","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Huang","year":"2018","unstructured":"Huang, H., Huang, Q., Kr\u00e4henb\u00fchl, P.: Domain transfer through deep activation matching. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 611\u2013626. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_36"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: ICML (2017)","DOI":"10.1109\/CVPR.2018.00392"},{"key":"21_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-46493-0_36","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Ghifary","year":"2016","unstructured":"Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 597\u2013613. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_36"},{"key":"21_CR29","unstructured":"Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: NIPS (2016)"},{"issue":"1","key":"21_CR30","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 1\u201335 (2016). 2096-2030","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30645-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:04:29Z","timestamp":1693785869000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30645-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030306441","9783030306458"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30645-8_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"2 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trento","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/event.unitn.it\/iciap2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"207","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"117","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}