{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:51:10Z","timestamp":1761648670093,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"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_36","type":"book-chapter","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T08:08:15Z","timestamp":1567584495000},"page":"390-401","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency"],"prefix":"10.1007","author":[{"given":"Massimiliano","family":"Mancini","sequence":"first","affiliation":[]},{"given":"Lorenzo","family":"Porzi","sequence":"additional","affiliation":[]},{"given":"Fabio","family":"Cermelli","sequence":"additional","affiliation":[]},{"given":"Barbara","family":"Caputo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,2]]},"reference":[{"issue":"1","key":"36_CR1","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79(1), 151\u2013175 (2010)","journal-title":"Mach. Learn."},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.18"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Carlucci, F.M., D\u2019Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00233"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Carlucci, F.M., Porzi, L., Caputo, B., Ricci, E., Rota Bul\u00f2, S.: Autodial: automatic domain alignment layers. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.542"},{"key":"36_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/978-3-319-68560-1_32","volume-title":"Image Analysis and Processing - ICIAP 2017","author":"FM Carlucci","year":"2017","unstructured":"Carlucci, F.M., Porzi, L., Caputo, B., Ricci, E., Bul\u00f2, S.R.: Just DIAL: domain alignment layers for unsupervised domain adaptation. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 357\u2013369. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68560-1_32"},{"key":"36_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-030-12939-2_14","volume-title":"Pattern Recognition","author":"A D\u2019Innocente","year":"2019","unstructured":"D\u2019Innocente, A., Caputo, B.: Domain generalization with domain-specific aggregation modules. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 187\u2013198. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12939-2_14"},{"key":"36_CR7","unstructured":"Donahue, J., et al.: Decaf: a deep convolutional activation feature for generic visual recognition. In: ICML (2014)"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Duan, L., Tsang, I.W., Xu, D., Chua, T.S.: Domain adaptation from multiple sources via auxiliary classifiers. In: ICML (2009)","DOI":"10.1145\/1553374.1553411"},{"key":"36_CR9","unstructured":"French, G., Mackiewicz, M., Fisher, M.: Self-ensembling for visual domain adaptation. In: ICLR (2018)"},{"key":"36_CR10","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2015)"},{"key":"36_CR11","unstructured":"Gong, B., Grauman, K., Sha, F.: Reshaping visual datasets for domain adaptation. In: NIPS (2013)"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1007\/978-3-642-33709-3_50","volume-title":"Computer Vision \u2013 ECCV 2012","author":"J Hoffman","year":"2012","unstructured":"Hoffman, J., Kulis, B., Darrell, T., Saenko, K.: Discovering latent domains for multisource domain adaptation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 702\u2013715. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33709-3_50"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Huang, J., Gretton, A., Borgwardt, K.M., Sch\u00f6lkopf, B., Smola, A.J.: Correcting sample selection bias by unlabeled data. In: NIPS (2006)","DOI":"10.7551\/mitpress\/7503.003.0080"},{"key":"36_CR15","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM-Multimedia, pp. 675\u2013678. ACM (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"36_CR17","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097\u20131105 (2012)"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.591"},{"issue":"5","key":"36_CR19","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1109\/TPAMI.2017.2704624","volume":"40","author":"W Li","year":"2018","unstructured":"Li, W., Xu, Z., Xu, D., Dai, D., Van Gool, L.: Domain generalization and adaptation using low rank exemplar SVMs. IEEE T-PAMI 40(5), 1114\u20131127 (2018)","journal-title":"IEEE T-PAMI"},{"key":"36_CR20","unstructured":"Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Revisiting batch normalization for practical domain adaptation. arXiv preprint arXiv:1603.04779 (2016)"},{"key":"36_CR21","unstructured":"Long, M., Wang, J.: Learning transferable features with deep adaptation networks. In: ICML (2015)"},{"key":"36_CR22","unstructured":"Long, M., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: NIPS (2016)"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Mancini, M., Bul\u00f2, S.R., Caputo, B., Ricci, E.: Best sources forward: domain generalization through source-specific nets. In: ICIP (2018)","DOI":"10.1109\/ICIP.2018.8451318"},{"issue":"3","key":"36_CR24","first-page":"2093","volume":"3","author":"M Mancini","year":"2018","unstructured":"Mancini, M., Bul\u00f2, S.R., Caputo, B., Ricci, E.: Robust place categorization with deep domain generalization. IEEE RAL 3(3), 2093\u20132100 (2018)","journal-title":"IEEE RAL"},{"key":"36_CR25","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":"36_CR26","doi-asserted-by":"crossref","unstructured":"Mancini, M., Karaoguz, H., Ricci, E., Jensfelt, P., Caputo, B.: Kitting in the wild through online domain adaptation. In: IROS (2018)","DOI":"10.1109\/IROS.2018.8593862"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Mancini, M., Porzi, L., Bul\u00f2, S.R., Caputo, B., Ricci, E.: Boosting domain adaptation by discovering latent domains. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00397"},{"key":"36_CR28","unstructured":"Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: Learning bounds and algorithms. arXiv preprint arXiv:0902.3430 (2009)"},{"issue":"10","key":"36_CR29","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Roy, S., Siarohin, A., Sangineto, E., Bulo, 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":"36_CR31","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":"36_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-642-15561-1_16","volume-title":"Computer Vision \u2013 ECCV 2010","author":"K Saenko","year":"2010","unstructured":"Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213\u2013226. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_16"},{"key":"36_CR33","doi-asserted-by":"crossref","unstructured":"Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. arXiv preprint arXiv:1702.08400 (2017)","DOI":"10.1109\/CVPR.2018.00392"},{"issue":"1","key":"36_CR34","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR35","unstructured":"Sun, Q., Chattopadhyay, R., Panchanathan, S., Ye, J.: A two-stage weighting framework for multi-source domain adaptation. In: NIPS (2011)"},{"key":"36_CR36","doi-asserted-by":"crossref","unstructured":"Xiong, C., McCloskey, S., Hsieh, S.H., Corso, J.J.: Latent domains modeling for visual domain adaptation. In: AAAI (2014)","DOI":"10.1609\/aaai.v28i1.9136"},{"key":"36_CR37","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":"36_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-10578-9_41","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Z Xu","year":"2014","unstructured":"Xu, Z., Li, W., Niu, L., Xu, D.: Exploiting low-rank structure from latent domains for domain generalization. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 628\u2013643. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_41"},{"key":"36_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-319-10578-9_31","volume-title":"Computer Vision \u2013 ECCV 2014","author":"X Zeng","year":"2014","unstructured":"Zeng, X., Ouyang, W., Wang, M., Wang, X.: Deep learning of scene-specific classifier for pedestrian detection. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 472\u2013487. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_31"}],"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_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:06:24Z","timestamp":1693785984000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30645-8_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030306441","9783030306458"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30645-8_36","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"}]}}