{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T02:42:01Z","timestamp":1775011321471,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":12,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819688883","type":"print"},{"value":"9789819688890","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"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":[[2026]]},"DOI":"10.1007\/978-981-96-8889-0_28","type":"book-chapter","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T08:57:29Z","timestamp":1751273849000},"page":"323-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Guided by Uncertainty: Semi-supervised Domain Adaptation with Curriculum and Contrastive Learning"],"prefix":"10.1007","author":[{"given":"Sunhyeok","family":"Hwang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seoung Bum","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101869","volume":"99","author":"A Mehrish","year":"2023","unstructured":"Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., Poria, S.: A review of deep learning techniques for speech processing. Inf. Fusion 99, 101869 (2023)","journal-title":"Inf. Fusion"},{"issue":"Suppl 1","key":"28_CR2","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2023","unstructured":"Gawlikowski, J., et al.: A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56(Suppl 1), 1513\u20131589 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"28_CR3","doi-asserted-by":"publisher","first-page":"6973","DOI":"10.1109\/ACCESS.2023.3237025","volume":"11","author":"P Singhal","year":"2023","unstructured":"Singhal, P., Walambe, R., Ramanna, S., Kotecha, K.: Domain adaptation: challenges, methods, datasets, and applications. IEEE Access 11, 6973\u20137020 (2023)","journal-title":"IEEE Access"},{"key":"28_CR4","first-page":"5089","volume":"34","author":"A Singh","year":"2021","unstructured":"Singh, A.: Clda: contrastive learning for semi-supervised domain adaptation. Adv. Neural. Inf. Process. Syst. 34, 5089\u20135101 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"59","key":"28_CR5","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(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"28_CR6","unstructured":"Berthelot, D., Roelofs, R., Sohn, K., Carlini, N., Kurakin, A.: Adamatch: a unified approach to semi-supervised learning and domain adaptation. arXiv preprint arXiv: 2106.04732 (2021)"},{"key":"28_CR7","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":"28_CR8","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp. 41\u201348 (2009, June)","DOI":"10.1145\/1553374.1553380"},{"key":"28_CR9","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":"28_CR10","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 1406\u20131415 (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Saito, K., Kim, D., Sclaroff, S., Darrell, T., Saenko, K.: Semi-supervised domain adaptation via minimax entropy. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 8050\u20138058 (2019)","DOI":"10.1109\/ICCV.2019.00814"},{"key":"28_CR12","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. Adv. Neural Inf. Process. Syst. (2004)"}],"container-title":["Lecture Notes in Computer Science","Advances and Trends in Artificial Intelligence. Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8889-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T01:32:52Z","timestamp":1775007172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8889-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"ISBN":["9789819688883","9789819688890"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8889-0_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"1 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IEA\/AIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kytakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ieaaie2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.i-somet.org\/iea-aie2025\/committees.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}