{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:27:27Z","timestamp":1778257647010,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["Nos. 62136004, 62276130"],"award-info":[{"award-number":["Nos. 62136004, 62276130"]}]},{"name":"the National Research Foundation, Singapore under its AI Singapore Programme","award":["AISG2-RP-2021-027"],"award-info":[{"award-number":["AISG2-RP-2021-027"]}]},{"name":"the National Key R\\&D Program of China","award":["No. 2023YFF1204803"],"award-info":[{"award-number":["No. 2023YFF1204803"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709239","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:44:43Z","timestamp":1743792283000},"page":"390-401","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2611-3145","authenticated-orcid":false,"given":"Peiliang","family":"Gong","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2138-4395","authenticated-orcid":false,"given":"Mohamed","family":"Ragab","sequence":"additional","affiliation":[{"name":"Technology Innovation Institute, Masdar, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1719-0328","authenticated-orcid":false,"given":"Zhenghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore &amp; Centre for Frontier AI Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6911-8256","authenticated-orcid":false,"given":"Yongyi","family":"Su","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, China &amp; Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0762-6562","authenticated-orcid":false,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore &amp; Centre for Frontier AI Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5658-7643","authenticated-orcid":false,"given":"Daoqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proceedings of the European Symposium on Artificial Neural Networks","volume":"3","author":"Anguita Davide","year":"2013","unstructured":"Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al. 2013. A public domain dataset for human activity recognition using smartphones. In Proceedings of the European Symposium on Artificial Neural Networks, Vol. 3. 437--442."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00816"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00349"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16846"},{"key":"e_1_3_2_2_5_1","volume-title":"This looks like that: deep learning for interpretable image recognition. Advances in neural information processing systems","author":"Chen Chaofan","year":"2019","unstructured":"Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. 2019. This looks like that: deep learning for interpretable image recognition. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00039"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00814"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3308189"},{"key":"e_1_3_2_2_10_1","volume-title":"Large margin deep networks for classification. Advances in neural information processing systems","author":"Elsayed Gamaleldin","year":"2018","unstructured":"Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio. 2018. Large margin deep networks for classification. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_11_1","volume-title":"Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley.","author":"Goldberger Ary L","year":"2000","unstructured":"Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, Vol. 101, 23 (2000), e215--e220."},{"key":"e_1_3_2_2_12_1","first-page":"27253","article-title":"Note: Robust continual test-time adaptation against temporal correlation","volume":"35","author":"Gong Taesik","year":"2022","unstructured":"Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, and Sung-Ju Lee. 2022. Note: Robust continual test-time adaptation against temporal correlation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 27253--27266.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_13_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Gong Taesik","year":"2024","unstructured":"Taesik Gong, Yewon Kim, Taeckyung Lee, Sorn Chottananurak, and Sung-Ju Lee. 2024. SoTTA: Robust Test-Time Adaptation on Noisy Data Streams. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_14_1","first-page":"6204","article-title":"Test time adaptation via conjugate pseudo-labels","volume":"35","author":"Goyal Sachin","year":"2022","unstructured":"Sachin Goyal, Mingjie Sun, Aditi Raghunathan, and J Zico Kolter. 2022. Test time adaptation via conjugate pseudo-labels. Advances in Neural Information Processing Systems, Vol. 35 (2022), 6204--6218.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_15_1","volume-title":"Domain Adaptation for Time Series Under Feature and Label Shifts. In International Conference on Machine Learning.","author":"He Huan","year":"2023","unstructured":"Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, and Marinka Zitnik. 2023. Domain Adaptation for Time Series Under Feature and Label Shifts. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00885"},{"key":"e_1_3_2_2_17_1","first-page":"2427","article-title":"Test-time classifier adjustment module for model-agnostic domain generalization","volume":"34","author":"Iwasawa Yusuke","year":"2021","unstructured":"Yusuke Iwasawa and Yutaka Matsuo. 2021. Test-time classifier adjustment module for model-agnostic domain generalization. Advances in Neural Information Processing Systems, Vol. 34 (2021), 2427--2440.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_18_1","volume-title":"Internetional Conference on Learning Representations.","author":"Jang Minguk","year":"2023","unstructured":"Minguk Jang, Sae-Young Chung, and Hye Won Chung. 2023. Test-time adaptation via self-training with nearest neighbor information. In Internetional Conference on Learning Representations."},{"key":"e_1_3_2_2_19_1","volume-title":"International Conference on Machine Learning. PMLR, 10280--10297","author":"Jin Xiaoyong","year":"2022","unstructured":"Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, and Yuyang Wang. 2022. Domain adaptation for time series forecasting via attention sharing. In International Conference on Machine Learning. PMLR, 10280--10297."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01343"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2023-1282"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29210"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/WASPAA52581.2021.9632771"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3110179"},{"key":"e_1_3_2_2_25_1","volume-title":"Workshop on challenges in representation learning, ICML","volume":"3","author":"Dong-Hyun","unstructured":"Dong-Hyun Lee et al. 2013. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, Vol. 3. Atlanta, 896."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.36001\/phme.2016.v3i1.1577"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00251"},{"key":"e_1_3_2_2_28_1","volume-title":"International Journal of Computer Vision","author":"Liang Jian","year":"2024","unstructured":"Jian Liang, Ran He, and Tieniu Tan. 2024. A comprehensive survey on test-time adaptation under distribution shifts. International Journal of Computer Vision (2024), 1--34."},{"key":"e_1_3_2_2_29_1","volume-title":"International conference on machine learning. PMLR, 6028--6039","author":"Liang Jian","year":"2020","unstructured":"Jian Liang, Dapeng Hu, and Jiashi Feng. 2020. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In International conference on machine learning. PMLR, 6028--6039."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.61115"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02198"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.01.062"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Qiao Liu and Hui Xue. 2021. Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation.. In IJCAI. 2744--2750.","DOI":"10.24963\/ijcai.2021\/378"},{"key":"e_1_3_2_2_34_1","first-page":"21808","article-title":"Ttt: When does self-supervised test-time training fail or thrive","volume":"34","author":"Liu Yuejiang","year":"2021","unstructured":"Yuejiang Liu, Parth Kothari, Bastien Van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi. 2021. Ttt: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, Vol. 34 (2021), 21808--21820.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i2.20081"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01469"},{"key":"e_1_3_2_2_37_1","volume-title":"International conference on machine learning. PMLR, 16888--16905","author":"Niu Shuaicheng","year":"2022","unstructured":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan. 2022. Efficient test-time model adaptation without forgetting. In International conference on machine learning. PMLR, 16888--16905."},{"key":"e_1_3_2_2_38_1","volume-title":"Towards Stable Test-Time Adaptation in Dynamic Wild World. In Internetional Conference on Learning Representations.","author":"Niu Shuaicheng","year":"2023","unstructured":"Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan. 2023. Towards Stable Test-Time Adaptation in Dynamic Wild World. In Internetional Conference on Learning Representations."},{"key":"e_1_3_2_2_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Press Ori","year":"2024","unstructured":"Ori Press, Steffen Schneider, Matthias K\u00fcmmerer, and Matthias Bethge. 2024. Rdumb: A simple approach that questions our progress in continual test-time adaptation. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3183252"},{"key":"e_1_3_2_2_41_1","volume-title":"Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li.","author":"Ragab Mohamed","year":"2023","unstructured":"Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, and Xiaoli Li. 2023a. Adatime: A benchmarking suite for domain adaptation on time series data. ACM Transactions on Knowledge Discovery from Data, Vol. 17, 8 (2023), 1--18."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599507"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"e_1_3_2_2_44_1","volume-title":"IEEE Transactions on Cybernetics","author":"Ren Lei","year":"2023","unstructured":"Lei Ren and Xuejun Cheng. 2023. Single\/Multi-Source Black-Box Domain Adaption for Sensor Time Series Data. IEEE Transactions on Cybernetics (2023)."},{"key":"e_1_3_2_2_45_1","volume-title":"Improving robustness against common corruptions by covariate shift adaptation. Advances in neural information processing systems","author":"Schneider Steffen","year":"2020","unstructured":"Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge. 2020. Improving robustness against common corruptions by covariate shift adaptation. Advances in neural information processing systems, Vol. 33 (2020), 11539--11551."},{"key":"e_1_3_2_2_46_1","volume-title":"Prototypical networks for few-shot learning. Advances in neural information processing systems","author":"Snell Jake","year":"2017","unstructured":"Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_47_1","first-page":"17543","article-title":"Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering","volume":"35","author":"Su Yongyi","year":"2022","unstructured":"Yongyi Su, Xun Xu, and Kui Jia. 2022. Revisiting realistic test-time training: Sequential inference and adaptation by anchored clustering. Advances in Neural Information Processing Systems, Vol. 35 (2022), 17543--17555.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_48_1","volume-title":"Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization. The 38th AAAI Conference on Artificial Intelligence","author":"Su Yongyi","year":"2024","unstructured":"Yongyi Su, Xun Xu, and Kui Jia. 2024. Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization. The 38th AAAI Conference on Artificial Intelligence (2024)."},{"key":"e_1_3_2_2_49_1","volume-title":"International Conference on Learning Representations","author":"Sukhbaatar Sainbayar","year":"2015","unstructured":"Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus. 2015. Training convolutional networks with noisy labels. International Conference on Learning Representations (2015)."},{"key":"e_1_3_2_2_50_1","volume-title":"International conference on machine learning. PMLR, 9229--9248","author":"Sun Yu","year":"2020","unstructured":"Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt. 2020. Test-time training with self-supervision for generalization under distribution shifts. In International conference on machine learning. PMLR, 9229--9248."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02193"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3136755.3136817"},{"key":"e_1_3_2_2_53_1","volume-title":"Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726","author":"Wang Dequan","year":"2020","unstructured":"Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. 2020. Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726 (2020)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00706"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01920"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01720"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3400066"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403228"},{"key":"e_1_3_2_2_59_1","volume-title":"Janardhan Rao Doppa, and Diane J Cook","author":"Wilson Garrett","year":"2023","unstructured":"Garrett Wilson, Janardhan Rao Doppa, and Diane J Cook. 2023. Calda: Improving multi-source time series domain adaptation with contrastive adversarial learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_2_60_1","volume-title":"Adversarial kinetic prototype framework for open set recognition","author":"Xia Ziheng","year":"2023","unstructured":"Ziheng Xia, Penghui Wang, Ganggang Dong, and Hongwei Liu. 2023. Adversarial kinetic prototype framework for open set recognition. IEEE Transactions on Neural Networks and Learning Systems (2023)."},{"key":"e_1_3_2_2_61_1","volume-title":"Temporal Attention Source-Free Adaptation for Chemical Processes Fault Diagnosis","author":"Xiao Yutang","year":"2023","unstructured":"Yutang Xiao, Hongbo Shi, Bing Song, Yang Tao, Shuai Tan, and Boyu Wang. 2023. Temporal Attention Source-Free Adaptation for Chemical Processes Fault Diagnosis. IEEE Transactions on Industrial Informatics (2023)."},{"key":"e_1_3_2_2_62_1","first-page":"21969","article-title":"Attribute prototype network for zero-shot learning","volume":"33","author":"Xu Wenjia","year":"2020","unstructured":"Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, and Zeynep Akata. 2020. Attribute prototype network for zero-shot learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 21969--21980.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_63_1","first-page":"2358","article-title":"Convolutional prototype network for open set recognition","volume":"44","author":"Yang Hong-Ming","year":"2020","unstructured":"Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, Qing Yang, and Cheng-Lin Liu. 2020. Convolutional prototype network for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 5 (2020), 2358--2370.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01528"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01362"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00375"},{"key":"e_1_3_2_2_67_1","first-page":"38629","article-title":"Memo: Test time robustness via adaptation and augmentation","volume":"35","author":"Zhang Marvin","year":"2022","unstructured":"Marvin Zhang, Sergey Levine, and Chelsea Finn. 2022. Memo: Test time robustness via adaptation and augmentation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 38629--38642.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_68_1","volume-title":"International Conference on Machine Learning. PMLR, 41647--41676","author":"Zhang Yifan","year":"2023","unstructured":"Yifan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan. 2023. Adanpc: Exploring non-parametric classifier for test-time adaptation. In International Conference on Machine Learning. PMLR, 41647--41676."},{"key":"e_1_3_2_2_69_1","volume-title":"Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems","author":"Zhang Zhilu","year":"2018","unstructured":"Zhilu Zhang and Mert Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01921"},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00581"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709239","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709239","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:40:11Z","timestamp":1755358811000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":71,"alternative-id":["10.1145\/3690624.3709239","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709239","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}