{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T15:13:13Z","timestamp":1761664393827,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030921842"},{"type":"electronic","value":"9783030921859"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92185-9_5","type":"book-chapter","created":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T17:02:46Z","timestamp":1638723766000},"page":"54-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Focally Discriminative Loss for\u00a0Unsupervised Domain Adaptation"],"prefix":"10.1007","author":[{"given":"Dongting","family":"Sun","sequence":"first","affiliation":[]},{"given":"Mengzhu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xurui","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Tianming","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"5_CR1","unstructured":"Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M.: Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 (2014)"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., Tian, Q.: Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3941\u20133950 (2020)","DOI":"10.1109\/CVPR42600.2020.00400"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Deng, W., Zheng, L., Sun, Y., Jiao, J.: Rethinking triplet loss for domain adaptation. IEEE Trans. Circuits Syst. Video Technol. 31(1), 29\u201337 (2020)","DOI":"10.1109\/TCSVT.2020.2968484"},{"key":"5_CR4","unstructured":"Donahue, J., et al.: A deep convolutional activation feature for generic visual recognition. UC Berkeley & ICSI, Berkeley, CA, USA"},{"issue":"1","key":"5_CR5","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030\u20132096 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR6","unstructured":"Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066\u20132073. IEEE (2012)"},{"issue":"1","key":"5_CR7","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13(1), 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR9","unstructured":"Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97\u2013105. PMLR (2015)"},{"key":"5_CR10","unstructured":"Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems, pp. 1640\u20131650 (2018)"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200\u20132207 (2013)","DOI":"10.1109\/ICCV.2013.274"},{"key":"5_CR12","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Advances in Neural Information Processing Systems, pp. 136\u2013144 (2016)"},{"issue":"2","key":"5_CR13","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199\u2013210 (2010)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"10","key":"5_CR14","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. knowl. Data Eng."},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Pei, Z., Cao, Z., Long, M., Wang, J.: Multi-adversarial domain adaptation. arXiv preprint arXiv:1809.02176 (2018)","DOI":"10.1609\/aaai.v32i1.11767"},{"key":"5_CR16","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":"5_CR17","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"5_CR18","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":"5_CR19","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018\u20135027 (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Wang, H., Yang, W., Wang, J., Wang, R., Lan, L., Geng, M.: Pairwise similarity regularization for adversarial domain adaptation. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2409\u20132418 (2020)","DOI":"10.1145\/3394171.3413516"},{"key":"5_CR21","unstructured":"Wang, M., Zhang, X., Lan, L., Wang, W., Tan, H., Luo, Z.: Improving unsupervised domain adaptation by reducing bi-level feature redundancy. arXiv preprint arXiv:2012.15732 (2020)"},{"key":"5_CR22","unstructured":"Wang, W., Li, H., Ding, Z., Wang, Z.: Rethink maximum mean discrepancy for domain adaptation. arXiv preprint arXiv:2007.00689 (2020)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, L., Ye, W., Long, M., Wang, J.: Transferable attention for domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5345\u20135352 (2019)","DOI":"10.1609\/aaai.v33i01.33015345"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Xu, R., Li, G., Yang, J., Lin, L.: Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1426\u20131435 (2019)","DOI":"10.1109\/ICCV.2019.00151"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Yang, X., Dong, J., Cao, Y., Wang, X., Wang, M., Chua, T.S.: Tree-augmented cross-modal encoding for complex-query video retrieval. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1339\u20131348 (2020)","DOI":"10.1145\/3397271.3401151"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Yang, X., Feng, F., Ji, W., Wang, M., Chua, T.S.: Deconfounded video moment retrieval with causal intervention. In: SIGIR (2021)","DOI":"10.1145\/3404835.3462823"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Yang, X., He, X., Wang, X., Ma, Y., Feng, F., Wang, M., Chua, T.S.: Interpretable fashion matching with rich attributes. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 775\u2013784 (2019)","DOI":"10.1145\/3331184.3331242"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ouyang, W., Li, W., Xu, D.: Collaborative and adversarial network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3801\u20133809 (2018)","DOI":"10.1109\/CVPR.2018.00400"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhang, X., Liao, Q., Yang, W., Lan, L., Luo, Z.: Robust normalized squares maximization for unsupervised domain adaptation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2317\u20132320 (2020)","DOI":"10.1145\/3340531.3412083"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, H., Jia, K., Tan, M.: Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5031\u20135040 (2019)","DOI":"10.1109\/CVPR.2019.00517"},{"issue":"8","key":"5_CR31","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1109\/TKDE.2018.2861858","volume":"31","author":"S Zheng","year":"2018","unstructured":"Zheng, S., Ding, C., Nie, F., Huang, H.: Harmonic mean linear discriminant analysis. IEEE Trans. Knowl. Data Eng. 31(8), 1520\u20131531 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhuang, F., Wang, J., Ke, G., Chen, J., Bian, J., Xiong, H., He, Q.: Deep subdomain adaptation network for image classification. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1713\u20131722 (2020)","DOI":"10.1109\/TNNLS.2020.2988928"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92185-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:40:57Z","timestamp":1710355257000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92185-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030921842","9783030921859"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92185-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}