{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:08:52Z","timestamp":1780765732691,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":72,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680664","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"876-885","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["CLIPCleaner: Cleaning Noisy Labels with CLIP"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9199-559X","authenticated-orcid":false,"given":"Chen","family":"Feng","sequence":"first","affiliation":[{"name":"Queen Mary University London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1803-5338","authenticated-orcid":false,"given":"Georgios","family":"Tzimiropoulos","sequence":"additional","affiliation":[{"name":"Queen Mary University London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3913-4738","authenticated-orcid":false,"given":"Ioannis","family":"Patras","sequence":"additional","affiliation":[{"name":"Queen Mary, University of London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Machine Learning. PMLR, 312--321","author":"Arazo Eric","year":"2019","unstructured":"Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, and Kevin McGuinness. 2019. Unsupervised label noise modeling and loss correction. In International Conference on Machine Learning. PMLR, 312--321."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. PMLR, 540--550","author":"Bahri Dara","year":"2020","unstructured":"Dara Bahri, Heinrich Jiang, and Maya Gupta. 2020. Deep k-nn for noisy labels. In International Conference on Machine Learning. PMLR, 540--550."},{"key":"e_1_3_2_1_3_1","volume-title":"PLOT: Prompt Learning with Optimal Transport for Vision-Language Models. In The Eleventh International Conference on Learning Representations.","author":"Chen Guangyi","year":"2022","unstructured":"Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, and Kun Zhang. 2022. PLOT: Prompt Learning with Optimal Transport for Vision-Language Models. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i13.17363"},{"key":"e_1_3_2_1_5_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_1_6_1","volume-title":"Shell Xu Hu, and Johan AK Suykens","author":"Chen Yingyi","year":"2021","unstructured":"Yingyi Chen, Xi Shen, Shell Xu Hu, and Johan AK Suykens. 2021. Boosting Co-teaching with Compression Regularization for Label Noise. arXiv preprint arXiv:2104.13766 (2021)."},{"key":"e_1_3_2_1_7_1","volume-title":"Demystifying how self-supervised features improve training from noisy labels. arXiv preprint arXiv:2110.09022","author":"Cheng Hao","year":"2021","unstructured":"Hao Cheng, Zhaowei Zhu, Xing Sun, and Yang Liu. 2021. Demystifying how self-supervised features improve training from noisy labels. arXiv preprint arXiv:2110.09022 (2021)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956660"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01907"},{"key":"e_1_3_2_1_10_1","volume-title":"SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise. In 33rd British Machine Vision Conference 2022, BMVC 2022","author":"Feng Chen","year":"2022","unstructured":"Chen Feng, Georgios Tzimiropoulos, and Ioannis Patras. 2022. SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise. In 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022. BMVA Press."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3426994"},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 2206--2212","author":"Feng Lei","year":"2021","unstructured":"Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, and Bo An. 2021. Can cross entropy loss be robust to label noise?. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 2206--2212."},{"key":"e_1_3_2_1_13_1","volume-title":"Clip-adapter: Better vision-language models with feature adapters. arXiv preprint arXiv:2110.04544","author":"Gao Peng","year":"2021","unstructured":"Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao. 2021. Clip-adapter: Better vision-language models with feature adapters. arXiv preprint arXiv:2110.04544 (2021)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00179"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00203"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00232"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"e_1_3_2_1_18_1","unstructured":"Jacob Goldberger and Ehud Ben-Reuven. 2016. Training deep neural-networks using a noise adaptation layer. (2016)."},{"key":"e_1_3_2_1_19_1","volume-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels. arXiv preprint arXiv:1804.06872","author":"Han Bo","year":"2018","unstructured":"Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018. Co-teaching: Robust training of deep neural networks with extremely noisy labels. arXiv preprint arXiv:1804.06872 (2018)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_1_21_1","volume-title":"Using trusted data to train deep networks on labels corrupted by severe noise. arXiv preprint arXiv:1802.05300","author":"Hendrycks Dan","year":"2018","unstructured":"Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel. 2018. Using trusted data to train deep networks on labels corrupted by severe noise. arXiv preprint arXiv:1802.05300 (2018)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Zhizhong Huang Junping Zhang and Hongming Shan. 2023. Twin Contrastive Learning with Noisy Labels. In CVPR.","DOI":"10.1109\/CVPR52729.2023.01122"},{"key":"e_1_3_2_1_23_1","volume-title":"International conference on machine learning. PMLR, 4804--4815","author":"Jiang Lu","year":"2020","unstructured":"Lu Jiang, Di Huang, Mason Liu, and Weilong Yang. 2020. Beyond synthetic noise: Deep learning on controlled noisy labels. In International conference on machine learning. PMLR, 4804--4815."},{"key":"e_1_3_2_1_24_1","volume-title":"International Conference on Machine Learning. PMLR, 2304--2313","author":"Jiang Lu","year":"2018","unstructured":"Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei. 2018. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International Conference on Machine Learning. PMLR, 2304--2313."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00945"},{"key":"e_1_3_2_1_26_1","volume-title":"International Conference on Machine Learning. PMLR, 16377--16392","author":"Kim Jihye","year":"2023","unstructured":"Jihye Kim, Aristide Baratin, Yan Zhang, and Simon Lacoste-Julien. 2023. CrossSplit: mitigating label noise memorization through data splitting. In International Conference on Machine Learning. PMLR, 16377--16392."},{"key":"e_1_3_2_1_27_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Kim Jang-Hyun","year":"2024","unstructured":"Jang-Hyun Kim, Sangdoo Yun, and Hyun Oh Song. 2024. Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_28_1","first-page":"24137","article-title":"Fine samples for learning with noisy labels","volume":"34","author":"Kim Taehyeon","year":"2021","unstructured":"Taehyeon Kim, Jongwoo Ko, JinHwan Choi, Se-Young Yun, et al. 2021. Fine samples for learning with noisy labels. Advances in Neural Information Processing Systems, Vol. 34 (2021), 24137--24149.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_29_1","volume-title":"Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394","author":"Li Junnan","year":"2020","unstructured":"Junnan Li, Richard Socher, and Steven CH Hoi. 2020. Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020)."},{"key":"e_1_3_2_1_30_1","unstructured":"Junnan Li Caiming Xiong and Steven Hoi. 2020. Learning from Noisy Data with Robust Representation Learning. (2020)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"e_1_3_2_1_32_1","first-page":"24184","article-title":"Estimating noise transition matrix with label correlations for noisy multi-label learning","volume":"35","author":"Li Shikun","year":"2022","unstructured":"Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, and Tongliang Liu. 2022. Estimating noise transition matrix with label correlations for noisy multi-label learning. Advances in Neural Information Processing Systems, Vol. 35 (2022), 24184--24198.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_33_1","volume-title":"Webvision database: Visual learning and understanding from web data. arXiv preprint arXiv:1708.02862","author":"Li Wen","year":"2017","unstructured":"Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool. 2017. Webvision database: Visual learning and understanding from web data. arXiv preprint arXiv:1708.02862 (2017)."},{"key":"e_1_3_2_1_34_1","volume-title":"Early-learning regularization prevents memorization of noisy labels. arXiv preprint arXiv:2007.00151","author":"Liu Sheng","year":"2020","unstructured":"Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. 2020. Early-learning regularization prevents memorization of noisy labels. arXiv preprint arXiv:2007.00151 (2020)."},{"key":"e_1_3_2_1_35_1","volume-title":"International conference on machine learning. PMLR, 6543--6553","author":"Ma Xingjun","year":"2020","unstructured":"Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey. 2020. Normalized loss functions for deep learning with noisy labels. In International conference on machine learning. PMLR, 6543--6553."},{"key":"e_1_3_2_1_36_1","volume-title":"arXiv preprint arXiv:1706.02613","author":"Malach Eran","year":"2017","unstructured":"Eran Malach and Shai Shalev-Shwartz. 2017. Decoupling\" when to update\" from\" how to update\". arXiv preprint arXiv:1706.02613 (2017)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00333"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00654"},{"key":"e_1_3_2_1_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Park Dongmin","year":"2024","unstructured":"Dongmin Park, Seola Choi, Doyoung Kim, Hwanjun Song, and Jae-Gil Lee. 2024. Robust data pruning under label noise via maximizing re-labeling accuracy. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00392"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"e_1_3_2_1_42_1","volume-title":"International conference on machine learning. PMLR, 8748--8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763."},{"key":"e_1_3_2_1_43_1","volume-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685","author":"Sohn Kihyuk","year":"2020","unstructured":"Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel. 2020. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. arXiv preprint arXiv:2001.07685 (2020)."},{"key":"e_1_3_2_1_44_1","volume-title":"International Conference on Machine Learning. PMLR, 5907--5915","author":"Song Hwanjun","year":"2019","unstructured":"Hwanjun Song, Minseok Kim, and Jae-Gil Lee. 2019. Selfie: Refurbishing unclean samples for robust deep learning. In International Conference on Machine Learning. PMLR, 5907--5915."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00162"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00524"},{"key":"e_1_3_2_1_47_1","volume-title":"Combating label noise in deep learning using abstention. arXiv preprint arXiv:1905.10964","author":"Thulasidasan Sunil","year":"2019","unstructured":"Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, and Jamal Mohd-Yusof. 2019. Combating label noise in deep learning using abstention. arXiv preprint arXiv:1905.10964 (2019)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17213"},{"key":"e_1_3_2_1_49_1","volume-title":"ProMix: combating label noise via maximizing clean sample utility. arXiv preprint arXiv:2207.10276","author":"Wang Haobo","year":"2022","unstructured":"Haobo Wang, Ruixuan Xiao, Yiwen Dong, Lei Feng, and Junbo Zhao. 2022. ProMix: combating label noise via maximizing clean sample utility. arXiv preprint arXiv:2207.10276 (2022)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_3_2_1_51_1","volume-title":"Clip-td: Clip targeted distillation for vision-language tasks. arXiv preprint arXiv:2201.05729","author":"Wang Zhecan","year":"2022","unstructured":"Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou, Jianwei Yang, Xiyang Dai, Bin Xiao, Haoxuan You, Shih-Fu Chang, and Lu Yuan. 2022. Clip-td: Clip targeted distillation for vision-language tasks. arXiv preprint arXiv:2201.05729 (2022)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20056-4_30"},{"key":"e_1_3_2_1_53_1","volume-title":"A topological filter for learning with label noise. Advances in neural information processing systems","author":"Wu Pengxiang","year":"2020","unstructured":"Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, and Chao Chen. 2020. A topological filter for learning with label noise. Advances in neural information processing systems, Vol. 33 (2020), 21382--21393."},{"key":"e_1_3_2_1_54_1","volume-title":"NGC: A Unified Framework for Learning with Open-World Noisy Data. arXiv preprint arXiv:2108.11035","author":"Wu Zhi-Fan","year":"2021","unstructured":"Zhi-Fan Wu, Tong Wei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, and Yu-Feng Li. 2021. NGC: A Unified Framework for Learning with Open-World Noisy Data. arXiv preprint arXiv:2108.11035 (2021)."},{"key":"e_1_3_2_1_55_1","first-page":"3047","article-title":"Extended T: Learning With Mixed Closed-Set and Open-Set Noisy Labels","volume":"45","author":"Xia Xiaobo","year":"2022","unstructured":"Xiaobo Xia, Bo Han, Nannan Wang, Jiankang Deng, Jiatong Li, Yinian Mao, and Tongliang Liu. 2022. Extended T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, 3 (2022), 3047--3058.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_1_56_1","volume-title":"Sample selection with uncertainty of losses for learning with noisy labels. arXiv preprint arXiv:2106.00445","author":"Xia Xiaobo","year":"2021","unstructured":"Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, and Masashi Sugiyama. 2021. Sample selection with uncertainty of losses for learning with noisy labels. arXiv preprint arXiv:2106.00445 (2021)."},{"key":"e_1_3_2_1_57_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 2691--2699","author":"Xiao Tong","year":"2015","unstructured":"Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. 2015. Learning from massive noisy labeled data for image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2691--2699."},{"key":"e_1_3_2_1_58_1","volume-title":"A novel information-theoretic loss function for training deep nets robust to label noise. Advances in neural information processing systems","author":"Xu Yilun","year":"2019","unstructured":"Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang. 2019. L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00021"},{"key":"e_1_3_2_1_60_1","volume-title":"Dual t: Reducing estimation error for transition matrix in label-noise learning. Advances in neural information processing systems","author":"Yao Yu","year":"2020","unstructured":"Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, and Masashi Sugiyama. 2020. Dual t: Reducing estimation error for transition matrix in label-noise learning. Advances in neural information processing systems, Vol. 33 (2020), 7260--7271."},{"key":"e_1_3_2_1_61_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Ye Xichen","year":"2024","unstructured":"Xichen Ye, Xiaoqiang Li, Tong Liu, Yan Sun, Weiqin Tong, et al. 2024. Active Negative Loss Functions for Learning with Noisy Labels. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"e_1_3_2_1_63_1","volume-title":"International Conference on Machine Learning. PMLR, 7164--7173","author":"Yu Xingrui","year":"2019","unstructured":"Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama. 2019. How does disagreement help generalization against label corruption?. In International Conference on Machine Learning. PMLR, 7164--7173."},{"key":"e_1_3_2_1_64_1","volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00953"},{"key":"e_1_3_2_1_66_1","volume-title":"Learning with Feature-Dependent Label Noise: A Progressive Approach. arXiv preprint arXiv:2103.07756","author":"Zhang Yikai","year":"2021","unstructured":"Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, and Chao Chen. 2021. Learning with Feature-Dependent Label Noise: A Progressive Approach. arXiv preprint arXiv:2103.07756 (2021)."},{"key":"e_1_3_2_1_67_1","volume-title":"Generalized cross entropy loss for training deep neural networks with noisy labels. arXiv preprint arXiv:1805.07836","author":"Zhang Zhilu","year":"2018","unstructured":"Zhilu Zhang and Mert R Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. arXiv preprint arXiv:1805.07836 (2018)."},{"key":"e_1_3_2_1_68_1","volume-title":"Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels. arXiv preprint arXiv:2103.13646","author":"Zheltonozhskii Evgenii","year":"2021","unstructured":"Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany. 2021. Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels. arXiv preprint arXiv:2103.13646 (2021)."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"},{"key":"e_1_3_2_1_70_1","volume-title":"International Conference on Learning Representations.","author":"Zhou Tianyi","year":"2020","unstructured":"Tianyi Zhou, Shengjie Wang, and Jeff Bilmes. 2020. Robust curriculum learning: from clean label detection to noisy label self-correction. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_71_1","volume-title":"International conference on machine learning. PMLR, 12846--12856","author":"Zhou Xiong","year":"2021","unstructured":"Xiong Zhou, Xianming Liu, Junjun Jiang, Xin Gao, and Xiangyang Ji. 2021. Asymmetric loss functions for learning with noisy labels. In International conference on machine learning. PMLR, 12846--12856."},{"key":"e_1_3_2_1_72_1","volume-title":"International Conference on Machine Learning. PMLR, 27412--27427","author":"Zhu Zhaowei","year":"2022","unstructured":"Zhaowei Zhu, Zihao Dong, and Yang Liu. 2022. Detecting corrupted labels without training a model to predict. In International Conference on Machine Learning. PMLR, 27412--27427."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680664","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680664","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:57Z","timestamp":1750295877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":72,"alternative-id":["10.1145\/3664647.3680664","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680664","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}