{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T21:16:32Z","timestamp":1768425392210,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819549863","type":"print"},{"value":"9789819549870","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-4987-0_21","type":"book-chapter","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:29:18Z","timestamp":1768393758000},"page":"292-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ML-NC-TTT: Multi-layer Noise Contrastive Learning for\u00a0Test-Time Training"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6642-5210","authenticated-orcid":false,"given":"Cangning","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6568-1335","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0309-325X","authenticated-orcid":false,"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2709-1243","authenticated-orcid":false,"given":"Qiquan","family":"Quan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"21_CR1","first-page":"480","volume":"34","author":"J Aneja","year":"2021","unstructured":"Aneja, J., Schwing, A., Kautz, J., Vahdat, A.: A contrastive learning approach for training variational autoencoder priors. Adv. Neural. Inf. Process. Syst. 34, 480\u2013493 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR2","unstructured":"Bassily, R., Cortes, C., Mao, A., Mohri, M.: Differentially private domain adaptation with theoretical guarantees. In: Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net (2024)"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Boudiaf, M., Mueller, R., Ben\u00a0Ayed, I., Bertinetto, L.: Parameter-free online test-time adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8344\u20138353 (2022)","DOI":"10.1109\/CVPR52688.2022.00816"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Choe, S., Shin, A., Park, K., Choi, J., Park, G.: Open-set domain adaptation for semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024, pp. 23943\u201323953. IEEE (2024)","DOI":"10.1109\/CVPR52733.2024.02260"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"21_CR6","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Du, Z., Li, X., Li, F., Lu, K., Zhu, L., Li, J.: Domain-agnostic mutual prompting for unsupervised domain adaptation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024, pp. 23375\u201323384. IEEE (2024)","DOI":"10.1109\/CVPR52733.2024.02206"},{"key":"21_CR8","first-page":"29374","volume":"35","author":"Y Gandelsman","year":"2022","unstructured":"Gandelsman, Y., Sun, Y., Chen, X., Efros, A.: Test-time training with masked autoencoders. Adv. Neural. Inf. Process. Syst. 35, 29374\u201329385 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR9","unstructured":"Gutmann, M., Hyv\u00e4rinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 297\u2013304. JMLR Workshop and Conference Proceedings (2010)"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Hakim, G.A.V., et al.: Clust3: information invariant test-time training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6136\u20136145 (2023)","DOI":"10.1109\/ICCV51070.2023.00564"},{"key":"21_CR11","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":"21_CR12","unstructured":"Hu, D., Liang, J., Wang, X., Foo, C.: Pseudo-calibration: Improving predictive uncertainty estimation in unsupervised domain adaptation. In: Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024. OpenReview.net (2024)"},{"key":"21_CR13","first-page":"21808","volume":"34","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Kothari, P., Van Delft, B., Bellot-Gurlet, B., Mordan, T., Alahi, A.: Ttt++: when does self-supervised test-time training fail or thrive? Adv. Neural. Inf. Process. Syst. 34, 21808\u201321820 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR14","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":"21_CR15","unstructured":"Mnih, A., Kavukcuoglu, K.: Learning word embeddings efficiently with noise-contrastive estimation. Advances in neural information processing systems 26 (2013)"},{"key":"21_CR16","unstructured":"Nado, Z., Padhy, S., Sculley, D., D\u2019Amour, A., Lakshminarayanan, B., Snoek, J.: Evaluating prediction-time batch normalization for robustness under covariate shift. arXiv preprint arXiv:2006.10963 (2020)"},{"key":"21_CR17","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Osowiechi, D., et al.: Nc-ttt: A noise constrastive approach for test-time training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6078\u20136086 (2024)","DOI":"10.1109\/CVPR52733.2024.00581"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Osowiechi, D., Hakim, G.A.V., Noori, M., Cheraghalikhani, M., Ben\u00a0Ayed, I., Desrosiers, C.: Tttflow: unsupervised test-time training with normalizing flow. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2126\u20132134 (2023)","DOI":"10.1109\/WACV56688.2023.00216"},{"key":"21_CR20","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: International Conference on Machine Learning, pp. 9229\u20139248. PMLR (2020)"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Tang, S., Su, W., Ye, M., Zhu, X.: Source-free domain adaptation with frozen multimodal foundation model. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA, June 16-22, 2024, pp. 23711\u201323720. IEEE (2024)","DOI":"10.1109\/CVPR52733.2024.02238"},{"key":"21_CR22","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726 (2020)"},{"key":"21_CR23","unstructured":"Xiong, W., et al.: Achieving human parity in conversational speech recognition. arXiv preprint arXiv:1610.05256 (2016)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4987-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:29:24Z","timestamp":1768393764000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4987-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819549863","9789819549870"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4987-0_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"15 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}