{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T09:34:52Z","timestamp":1761989692813,"version":"3.40.3"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031198205"},{"type":"electronic","value":"9783031198212"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-19821-2_23","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T12:12:59Z","timestamp":1666440779000},"page":"402-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Embedding Contrastive Unsupervised Features to\u00a0Cluster In- And Out-of-Distribution Noise in\u00a0Corrupted Image Datasets"],"prefix":"10.1007","author":[{"given":"Paul","family":"Albert","sequence":"first","affiliation":[]},{"given":"Eric","family":"Arazo","sequence":"additional","affiliation":[]},{"given":"Noel E.","family":"O\u2019Connor","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"McGuinness","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Albert, P., Ortego, D., Arazo, E., O\u2019Connor, N., McGuinness, K.: Addressing out-of-distribution label noise in webly-labelled data. In: Winter Conference on Applications of Computer Vision (WACV) (2022)","DOI":"10.1109\/WACV51458.2022.00245"},{"issue":"2","key":"23_CR2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/304181.304187","volume":"28","author":"M Ankerst","year":"1999","unstructured":"Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28(2), 49\u201360 (1999)","journal-title":"ACM SIGMOD Rec."},{"key":"23_CR3","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N., McGuinness, K.: Unsupervised label noise modeling and loss correction. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"23_CR4","unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning (ICML) (2017)"},{"key":"23_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-020-00622-y","volume":"7","author":"H Borgli","year":"2020","unstructured":"Borgli, H., et al.: HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci. Data 7, 1\u201314 (2020)","journal-title":"Sci. Data"},{"key":"23_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"23_CR7","unstructured":"Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.: Big self-supervised models are strong semi-supervised learners. arXiv:2006.10029 (2020)"},{"key":"23_CR8","unstructured":"Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of ImageNet as an alternative to the CIFAR datasets. arXiv:1707.08819 (2017)"},{"key":"23_CR9","unstructured":"Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: International Conference on Artificial Intelligence and Statistics (AISTATS) (2011)"},{"key":"23_CR10","unstructured":"Cordeiro, F.R., Belagiannis, V., Reid, I., Carneiro, G.: PropMix: hard sample filtering and proportional MixUp for learning with noisy labels. arXiv:2110.11809 (2021)"},{"key":"23_CR11","unstructured":"Fort, S., Ren, J., Lakshminarayanan, B.: Exploring the limits of out-of-distribution detection. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"23_CR12","unstructured":"Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"23_CR13","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Trash to treasure: harvesting OOD data with cross-modal matching for open-set semi-supervised learning. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00820"},{"key":"23_CR15","unstructured":"Hwanjun, S., Minseok, K., Dongmin, P., Jae-Gil, L.: Learning from noisy labels with deep neural networks: a survey. arXiv:2007.08199 (2020)"},{"key":"23_CR16","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: International Conference on Machine Learning (ICML) (2018)"},{"key":"23_CR17","unstructured":"Jiang, L., Huang, D., Liu, M., Yang, W.: Beyond synthetic noise: deep learning on controlled noisy labels. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"23_CR18","unstructured":"Kaiming, H., Xiangyu, Z., Shaoqing, R., Jian, S.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)"},{"key":"23_CR19","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. University of Toronto, Technical report (2009)"},{"key":"23_CR20","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2012)"},{"key":"23_CR21","unstructured":"Lee, K., Zhu, Y., Sohn, K., Li, C.L., Shin, J., Lee, H.: i-Mix: a strategy for regularizing contrastive representation learning. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"23_CR22","unstructured":"Li, J., Socher, R., Hoi, S.: DivideMix: learning with noisy labels as semi-supervised learning. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Li, J., Xiong, C., Hoi, S.C.: Learning from noisy data with robust representation learning. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00935"},{"key":"23_CR24","unstructured":"Li, W., Wang, L., Li, W., Agustsson, E., Van Gool, L.: WebVision database: visual learning and understanding from web data. arXiv:1708.02862 (2017)"},{"key":"23_CR25","unstructured":"Liu, S., Niles-Weed, J., Razavian, N., Fernandez-Granda, C.: Early-learning regularization prevents memorization of noisy labels. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"23_CR26","unstructured":"Mingxing, T., Quoc, L.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"23_CR27","unstructured":"Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems (NeurIPS) (2002)"},{"key":"23_CR28","unstructured":"Ortego, D., Arazo, E., Albert, P., O\u2019Connor, N., McGuinness, K.: Towards robust learning with different label noise distributions. In: International Conference on Pattern Recognition (ICPR) (2020)"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Ortego, D., Arazo, E., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Multi-objective interpolation training for robustness to label noise. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00654"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.240"},{"key":"23_CR31","unstructured":"Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., Rabinovich, A.: Training deep neural networks on noisy labels with bootstrapping. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Sachdeva, R., Cordeiro, F.R., Belagiannis, V., Reid, I., Carneiro, G.: EvidentialMix: learning with combined open-set and closed-set noisy labels. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) (2020)","DOI":"10.1109\/WACV48630.2021.00365"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Sachdeva, R., Cordeiro, F.R., Belagiannis, V., Reid, I., Carneiro, G.: ScanMix: learning from severe label noise via semantic clustering and semi-supervised learning. arXiv:2103.11395 (2021)","DOI":"10.1016\/j.patcog.2022.109121"},{"key":"23_CR34","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"issue":"8","key":"23_CR35","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 22(8), 888\u2013905 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"23_CR36","unstructured":"Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: Advances in Neural Information Processing Systems (NeurIPS) (2016)"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Sun, Z., et al.: Webly supervised fine-grained recognition: benchmark datasets and an approach. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01043"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Association for the Advancement of Artificial Intelligence (AAAI) (2016)","DOI":"10.1609\/aaai.v31i1.11231"},{"issue":"5500","key":"23_CR39","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319\u20132323 (2000)","journal-title":"Science"},{"key":"23_CR40","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems (NeuRIPS) (2016)"},{"key":"23_CR41","unstructured":"Vyas, N., Saxena, S., Voice, T.: Learning soft labels via meta learning. arXiv:2009.09496 (2020)"},{"key":"23_CR42","unstructured":"Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: International Conference on Machine Learning (ICLR) (2020)"},{"key":"23_CR43","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Iterative learning with open-set noisy labels. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00906"},{"key":"23_CR44","unstructured":"Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)"},{"key":"23_CR45","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhu, L., Jiang, L., Yang, Y.: Faster meta update strategy for noise-robust deep learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00021"},{"key":"23_CR46","doi-asserted-by":"crossref","unstructured":"Yao, Y., et al.: Jo-SRC: a contrastive approach for combating noisy labels. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00515"},{"key":"23_CR47","doi-asserted-by":"crossref","unstructured":"Yu, Q., Aizawa, K.: Unsupervised out-of-distribution detection by maximum classifier discrepancy. In: IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00961"},{"key":"23_CR48","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires re-thinking generalization. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"23_CR49","unstructured":"Zhang, H., Cisse, M., Dauphin, Y., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (ICLR) (2018)"},{"issue":"6","key":"23_CR50","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452\u20131464 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR51","unstructured":"Zhou, T., Wang, S., Bilmes, J.: Robust curriculum learning: from clean label detection to noisy label self-correction. In: International Conference on Learning Representations (ICLR) (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19821-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T12:54:19Z","timestamp":1666443259000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19821-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198205","9783031198212"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19821-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}