{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:16:02Z","timestamp":1759331762450,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":75,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3547984","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:01Z","timestamp":1665416581000},"page":"4635-4644","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration"],"prefix":"10.1145","author":[{"given":"Manyi","family":"Zhang","sequence":"first","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Ren","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihao","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Yuan","sequence":"additional","affiliation":[{"name":"Tsinghua University &amp; Peng Cheng National Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Devansh Arpit Stanisaw Jastrzbski Nicolas Ballas David Krueger Emmanuel Bengio Maxinder S Kanwal Tegan Maharaj Asja Fischer Aaron Courville Yoshua Bengio etal 2017. A closer look at memorization in deep networks. In ICML. 233--242.  Devansh Arpit Stanisaw Jastrzbski Nicolas Ballas David Krueger Emmanuel Bengio Maxinder S Kanwal Tegan Maharaj Asja Fischer Aaron Courville Yoshua Bengio et al. 2017. A closer look at memorization in deep networks. In ICML. 233--242."},{"key":"e_1_3_2_1_2_1","unstructured":"Antonin Berthon Bo Han Gang Niu Tongliang Liu and Masashi Sugiyama. 2021. Confidence Scores Make Instance-dependent Label-noise learning possible. In ICML.  Antonin Berthon Bo Han Gang Niu Tongliang Liu and Masashi Sugiyama. 2021. Confidence Scores Make Instance-dependent Label-noise learning possible. In ICML."},{"key":"e_1_3_2_1_3_1","volume-title":"Guangyong Chen, and Shengyu Zhang.","author":"Chen Pengfei","year":"2019","unstructured":"Pengfei Chen , Ben Ben Liao , Guangyong Chen, and Shengyu Zhang. 2019 . Understanding and utilizing deep neural networks trained with noisy labels. In ICML. 1062--1070. Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang. 2019. Understanding and utilizing deep neural networks trained with noisy labels. In ICML. 1062--1070."},{"key":"e_1_3_2_1_4_1","volume-title":"Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. arXiv preprint arXiv:2012.05458","author":"Chen Pengfei","year":"2020","unstructured":"Pengfei Chen , Junjie Ye , Guangyong Chen , Jingwei Zhao , and Pheng-Ann Heng . 2020. Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. arXiv preprint arXiv:2012.05458 ( 2020 ). Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, and Pheng-Ann Heng. 2020. Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. arXiv preprint arXiv:2012.05458 (2020)."},{"key":"e_1_3_2_1_5_1","volume-title":"Learning with instance-dependent label noise: A sample sieve approach. arXiv preprint arXiv:2010.02347","author":"Cheng Hao","year":"2020","unstructured":"Hao Cheng , Zhaowei Zhu , Xingyu Li , Yifei Gong , Xing Sun , and Yang Liu . 2020. Learning with instance-dependent label noise: A sample sieve approach. arXiv preprint arXiv:2010.02347 ( 2020 ). Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu. 2020. Learning with instance-dependent label noise: A sample sieve approach. arXiv preprint arXiv:2010.02347 (2020)."},{"key":"e_1_3_2_1_6_1","unstructured":"Jiacheng Cheng Tongliang Liu Kotagiri Ramamohanarao and Dacheng Tao. 2020. Learning with bounded instance-and label-dependent label noise. In ICML.  Jiacheng Cheng Tongliang Liu Kotagiri Ramamohanarao and Dacheng Tao. 2020. Learning with bounded instance-and label-dependent label noise. In ICML."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00160"},{"key":"e_1_3_2_1_8_1","volume-title":"Recent advances in algorithmic high-dimensional robust statistics. arXiv preprint arXiv:1911.05911","author":"Diakonikolas Ilias","year":"2019","unstructured":"Ilias Diakonikolas and Daniel M Kane . 2019. Recent advances in algorithmic high-dimensional robust statistics. arXiv preprint arXiv:1911.05911 ( 2019 ). Ilias Diakonikolas and Daniel M Kane. 2019. Recent advances in algorithmic high-dimensional robust statistics. arXiv preprint arXiv:1911.05911 (2019)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","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 IJCAI. 2206--2212.  Lei Feng Senlin Shu Zhuoyi Lin Fengmao Lv Li Li and Bo An. 2021. Can cross entropy loss be robust to label noise?. In IJCAI. 2206--2212.","DOI":"10.24963\/ijcai.2020\/305"},{"volume-title":"Robust statistics: the approach based on influence functions","author":"Hampel Frank R","key":"e_1_3_2_1_10_1","unstructured":"Frank R Hampel , Elvezio M Ronchetti , Peter J Rousseeuw , and Werner A Stahel . 2011. Robust statistics: the approach based on influence functions . Vol. 196 . John Wiley & Sons . Frank R Hampel, Elvezio M Ronchetti, Peter J Rousseeuw, and Werner A Stahel. 2011. Robust statistics: the approach based on influence functions. Vol. 196. John Wiley & Sons."},{"key":"e_1_3_2_1_11_1","volume-title":"Sigua: Forgetting may make learning with noisy labels more robust. In ICML. 4006--4016.","author":"Han Bo","year":"2020","unstructured":"Bo Han , Gang Niu , Xingrui Yu , Quanming Yao , Miao Xu , Ivor Tsang , and Masashi Sugiyama . 2020 . Sigua: Forgetting may make learning with noisy labels more robust. In ICML. 4006--4016. Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, and Masashi Sugiyama. 2020. Sigua: Forgetting may make learning with noisy labels more robust. In ICML. 4006--4016."},{"key":"e_1_3_2_1_12_1","volume-title":"Masking: A new perspective of noisy supervision. In NeurIPS. 5836--5846.","author":"Han Bo","year":"2018","unstructured":"Bo Han , Jiangchao Yao , Gang Niu , Mingyuan Zhou , Ivor Tsang , Ya Zhang , and Masashi Sugiyama . 2018 . Masking: A new perspective of noisy supervision. In NeurIPS. 5836--5846. Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, and Masashi Sugiyama. 2018. Masking: A new perspective of noisy supervision. In NeurIPS. 5836--5846."},{"key":"e_1_3_2_1_13_1","unstructured":"Dan Hendrycks Mantas Mazeika Duncan Wilson and Kevin Gimpel. 2018. Using trusted data to train deep networks on labels corrupted by severe noise. In NeurIPS.  Dan Hendrycks Mantas Mazeika Duncan Wilson and Kevin Gimpel. 2018. Using trusted data to train deep networks on labels corrupted by severe noise. In NeurIPS."},{"key":"e_1_3_2_1_14_1","unstructured":"Daniel Hern\u00e1ndez-Lobato Viktoriia Sharmanska Kristian Kersting Christoph H Lampert and Novi Quadrianto. 2014. Mind the nuisance: Gaussian process classification using privileged noise. In NeurIPS. 837--845.  Daniel Hern\u00e1ndez-Lobato Viktoriia Sharmanska Kristian Kersting Christoph H Lampert and Novi Quadrianto. 2014. Mind the nuisance: Gaussian process classification using privileged noise. In NeurIPS. 837--845."},{"key":"e_1_3_2_1_15_1","unstructured":"Andrew Howard Andrey Zhmoginov Liang-Chieh Chen Mark Sandler and Menglong Zhu. 2018. Inverted residuals and linear bottlenecks: Mobile networks for classification detection and segmentation. (2018).  Andrew Howard Andrey Zhmoginov Liang-Chieh Chen Mark Sandler and Menglong Zhu. 2018. Inverted residuals and linear bottlenecks: Mobile networks for classification detection and segmentation. (2018)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Jinchi Huang Lie Qu Rongfei Jia and Binqiang Zhao. 2019. O2u-net: A simple noisy label detection approach for deep neural networks. In ICCV. 3326--3334.  Jinchi Huang Lie Qu Rongfei Jia and Binqiang Zhao. 2019. O2u-net: A simple noisy label detection approach for deep neural networks. In ICCV. 3326--3334.","DOI":"10.1109\/ICCV.2019.00342"},{"volume-title":"Breakthroughs in statistics","author":"Huber Peter J","key":"e_1_3_2_1_18_1","unstructured":"Peter J Huber . 1992. Robust estimation of a location parameter . In Breakthroughs in statistics . Springer , 492--518. Peter J Huber. 1992. Robust estimation of a location parameter. In Breakthroughs in statistics. Springer, 492--518."},{"volume-title":"Robust statistics","author":"Huber Peter J","key":"e_1_3_2_1_19_1","unstructured":"Peter J Huber . 2004. Robust statistics . Vol. 523 . John Wiley & Sons . Peter J Huber. 2004. Robust statistics. Vol. 523. John Wiley & Sons."},{"key":"e_1_3_2_1_20_1","unstructured":"Zhimeng Jiang Kaixiong Zhou Zirui Liu Li Li Rui Chen Soo-Hyun Choi and Xia Hu. 2022. An Information Fusion Approach to Learning with Instance-Dependent Label Noise. In ICLR.  Zhimeng Jiang Kaixiong Zhou Zirui Liu Li Li Rui Chen Soo-Hyun Choi and Xia Hu. 2022. An Information Fusion Approach to Learning with Instance-Dependent Label Noise. In ICLR."},{"key":"e_1_3_2_1_21_1","unstructured":"Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision?. In NeurIPS.  Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision?. In NeurIPS."},{"key":"e_1_3_2_1_22_1","unstructured":"Taehyeon Kim Jongwoo Ko JinHwan Choi Se-Young Yun etal 2021. FINE Samples for Learning with Noisy Labels. In NeurIPS.  Taehyeon Kim Jongwoo Ko JinHwan Choi Se-Young Yun et al. 2021. FINE Samples for Learning with Noisy Labels. In NeurIPS."},{"key":"e_1_3_2_1_23_1","volume-title":"Nlnl: Negative learning for noisy labels. In ICCV. 101--110.","author":"Kim Youngdong","year":"2019","unstructured":"Youngdong Kim , Junho Yim , Juseung Yun , and Junmo Kim . 2019 . Nlnl: Negative learning for noisy labels. In ICCV. 101--110. Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim. 2019. Nlnl: Negative learning for noisy labels. In ICCV. 101--110."},{"key":"e_1_3_2_1_24_1","unstructured":"Durk P Kingma Tim Salimans Rafal Jozefowicz Xi Chen Ilya Sutskever and Max Welling. 2016. Improved variational inference with inverse autoregressive flow. In NeurIPS.  Durk P Kingma Tim Salimans Rafal Jozefowicz Xi Chen Ilya Sutskever and Max Welling. 2016. Improved variational inference with inverse autoregressive flow. In NeurIPS."},{"key":"e_1_3_2_1_25_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2016.76"},{"key":"e_1_3_2_1_28_1","unstructured":"Kimin Lee Sukmin Yun Kibok Lee Honglak Lee Bo Li and Jinwoo Shin. 2019. Robust inference via generative classifiers for handling noisy labels. In ICML. 3763--3772.  Kimin Lee Sukmin Yun Kibok Lee Honglak Lee Bo Li and Jinwoo Shin. 2019. Robust inference via generative classifiers for handling noisy labels. In ICML. 3763--3772."},{"key":"e_1_3_2_1_29_1","volume-title":"Cleannet: Transfer learning for scalable image classifier training with label noise. In CVPR. 5447--5456.","author":"Lee Kuang-Huei","year":"2018","unstructured":"Kuang-Huei Lee , Xiaodong He , Lei Zhang , and Linjun Yang . 2018 . Cleannet: Transfer learning for scalable image classifier training with label noise. In CVPR. 5447--5456. Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang. 2018. Cleannet: Transfer learning for scalable image classifier training with label noise. In CVPR. 5447--5456."},{"key":"e_1_3_2_1_30_1","volume-title":"Hoi","author":"Li Junnan","year":"2020","unstructured":"Junnan Li , Richard Socher , and Steven C.H . Hoi . 2020 . DivideMix: Learning with Noisy Labels as Semi-supervised Learning. In ICLR. Junnan Li, Richard Socher, and Steven C.H. Hoi. 2020. DivideMix: Learning with Noisy Labels as Semi-supervised Learning. In ICLR."},{"key":"e_1_3_2_1_31_1","volume-title":"Uncertainty Modeling for Out-of-Distribution Generalization. arXiv preprint arXiv:2202.03958","author":"Li Xiaotong","year":"2022","unstructured":"Xiaotong Li , Yongxing Dai , Yixiao Ge , Jun Liu , Ying Shan , and Ling-Yu Duan . 2022. Uncertainty Modeling for Out-of-Distribution Generalization. arXiv preprint arXiv:2202.03958 ( 2022 ). Xiaotong Li, Yongxing Dai, Yixiao Ge, Jun Liu, Ying Shan, and Ling-Yu Duan. 2022. Uncertainty Modeling for Out-of-Distribution Generalization. arXiv preprint arXiv:2202.03958 (2022)."},{"key":"e_1_3_2_1_32_1","unstructured":"Xuefeng Li Tongliang Liu Bo Han Gang Niu and Masashi Sugiyama. 2021. Provably End-to-end Label-Noise Learning without Anchor Points. In ICML.  Xuefeng Li Tongliang Liu Bo Han Gang Niu and Masashi Sugiyama. 2021. Provably End-to-end Label-Noise Learning without Anchor Points. In ICML."},{"key":"e_1_3_2_1_33_1","unstructured":"Yuncheng Li Jianchao Yang Yale Song Liangliang Cao Jiebo Luo and Li-Jia Li. 2017. Learning from noisy labels with distillation. In ICCV. 1910--1918.  Yuncheng Li Jianchao Yang Yale Song Liangliang Cao Jiebo Luo and Li-Jia Li. 2017. Learning from noisy labels with distillation. In ICCV. 1910--1918."},{"key":"e_1_3_2_1_34_1","unstructured":"Sheng Liu Jonathan Niles-Weed Narges Razavian and Carlos Fernandez-Granda. 2020. Early-learning regularization prevents memorization of noisy labels. In NeurIPS.  Sheng Liu Jonathan Niles-Weed Narges Razavian and Carlos Fernandez-Granda. 2020. Early-learning regularization prevents memorization of noisy labels. In NeurIPS."},{"key":"e_1_3_2_1_35_1","volume-title":"Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. arXiv preprint arXiv:1910.03231","author":"Liu Yang","year":"2019","unstructured":"Yang Liu and Hongyi Guo . 2019. Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. arXiv preprint arXiv:1910.03231 ( 2019 ). Yang Liu and Hongyi Guo. 2019. Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. arXiv preprint arXiv:1910.03231 (2019)."},{"key":"e_1_3_2_1_36_1","unstructured":"Michal Lukasik Srinadh Bhojanapalli Aditya Menon and Sanjiv Kumar. 2020. Does label smoothing mitigate label noise?. In ICML. 6448--6458.  Michal Lukasik Srinadh Bhojanapalli Aditya Menon and Sanjiv Kumar. 2020. Does label smoothing mitigate label noise?. In ICML. 6448--6458."},{"key":"e_1_3_2_1_37_1","volume-title":"Tsang","author":"Lyu Yueming","year":"2020","unstructured":"Yueming Lyu and Ivor W . Tsang . 2020 . Curriculum Loss : Robust Learning and Generalization against Label Corruption. In ICLR. Yueming Lyu and Ivor W. Tsang. 2020. Curriculum Loss: Robust Learning and Generalization against Label Corruption. In ICLR."},{"key":"e_1_3_2_1_38_1","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 ICML. 6543--6553.  Xingjun Ma Hanxun Huang Yisen Wang Simone Romano Sarah Erfani and James Bailey. 2020. Normalized loss functions for deep learning with noisy labels. In ICML. 6543--6553."},{"volume-title":"Foundations of Machine Learning","author":"Mohri Mehryar","key":"e_1_3_2_1_39_1","unstructured":"Mehryar Mohri , Afshin Rostamizadeh , and Ameet Talwalkar . 2018. Foundations of Machine Learning . MIT Press . Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2018. Foundations of Machine Learning. MIT Press."},{"key":"e_1_3_2_1_40_1","volume-title":"Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox.","author":"Nguyen Duc Tam","year":"2020","unstructured":"Duc Tam Nguyen , Chaithanya Kumar Mummadi , Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox. 2020 . SELF : Learning to Filter Noisy Labels with Self-Ensembling. In ICLR. Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox. 2020. SELF: Learning to Filter Noisy Labels with Self-Ensembling. In ICLR."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Kento Nishi Yi Ding Alex Rich and Tobias Hollerer. 2021. Augmentation strategies for learning with noisy labels. In CVPR. 8022--8031.  Kento Nishi Yi Ding Alex Rich and Tobias Hollerer. 2021. Augmentation strategies for learning with noisy labels. In CVPR. 8022--8031.","DOI":"10.1109\/CVPR46437.2021.00793"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Diego Ortego Eric Arazo Paul Albert Noel E O'Connor and Kevin McGuinness. 2021. Multi-objective interpolation training for robustness to label noise. In CVPR. 6606--6615.  Diego Ortego Eric Arazo Paul Albert Noel E O'Connor and Kevin McGuinness. 2021. Multi-objective interpolation training for robustness to label noise. In CVPR. 6606--6615.","DOI":"10.1109\/CVPR46437.2021.00654"},{"key":"e_1_3_2_1_44_1","volume-title":"Richard Nock, and Lizhen Qu.","author":"Patrini Giorgio","year":"2017","unstructured":"Giorgio Patrini , Alessandro Rozza , Aditya Krishna Menon , Richard Nock, and Lizhen Qu. 2017 . Making deep neural networks robust to label noise: A loss correction approach. In CVPR. 1944--1952. Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. 2017. Making deep neural networks robust to label noise: A loss correction approach. In CVPR. 1944--1952."},{"key":"e_1_3_2_1_45_1","unstructured":"Geoff Pleiss Tianyi Zhang Ethan R Elenberg and Kilian Q Weinberger. 2020. Identifying mislabeled data using the area under the margin ranking. In NeurIPS.  Geoff Pleiss Tianyi Zhang Ethan R Elenberg and Kilian Q Weinberger. 2020. Identifying mislabeled data using the area under the margin ranking. In NeurIPS."},{"key":"e_1_3_2_1_46_1","volume-title":"Evidentialmix: Learning with combined open-set and closed-set noisy labels. In WACV. 3607--3615.","author":"Sachdeva Ragav","year":"2021","unstructured":"Ragav Sachdeva , Filipe R Cordeiro , Vasileios Belagiannis , Ian Reid , and Gustavo Carneiro . 2021 . Evidentialmix: Learning with combined open-set and closed-set noisy labels. In WACV. 3607--3615. Ragav Sachdeva, Filipe R Cordeiro, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro. 2021. Evidentialmix: Learning with combined open-set and closed-set noisy labels. In WACV. 3607--3615."},{"key":"e_1_3_2_1_47_1","unstructured":"Paul Hongsuck Seo Geeho Kim and Bohyung Han. 2019. Combinatorial inference against label noise. In NeurIPS. 1173--1183.  Paul Hongsuck Seo Geeho Kim and Bohyung Han. 2019. Combinatorial inference against label noise. In NeurIPS. 1173--1183."},{"volume-title":"Machine learning in nonstationary environments: Introduction to covariate shift adaptation","author":"Sugiyama Masashi","key":"e_1_3_2_1_48_1","unstructured":"Masashi Sugiyama and Motoaki Kawanabe . 2012. Machine learning in nonstationary environments: Introduction to covariate shift adaptation . MIT press . Masashi Sugiyama and Motoaki Kawanabe. 2012. Machine learning in nonstationary environments: Introduction to covariate shift adaptation. MIT press."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Christian Szegedy Sergey Ioffe Vincent Vanhoucke and Alexander A Alemi. 2017. Inception-v4 inception-resnet and the impact of residual connections on learning. In AAAI.  Christian Szegedy Sergey Ioffe Vincent Vanhoucke and Alexander A Alemi. 2017. Inception-v4 inception-resnet and the impact of residual connections on learning. In AAAI.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475622"},{"key":"e_1_3_2_1_51_1","volume-title":"International conference on machine learning. PMLR, 6105--6114","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le . 2019 . Efficientnet: Rethinking model scaling for convolutional neural networks . In International conference on machine learning. PMLR, 6105--6114 . Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. PMLR, 6105--6114."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Daiki Tanaka Daiki Ikami Toshihiko Yamasaki and Kiyoharu Aizawa. 2018. Joint Optimization Framework for Learning with Noisy Labels. In CVPR.  Daiki Tanaka Daiki Ikami Toshihiko Yamasaki and Kiyoharu Aizawa. 2018. Joint Optimization Framework for Learning with Noisy Labels. In CVPR.","DOI":"10.1109\/CVPR.2018.00582"},{"key":"e_1_3_2_1_53_1","unstructured":"Kiran K Thekumparampil Ashish Khetan Zinan Lin and Sewoong Oh. 2018. Robustness of conditional GANs to noisy labels. In NeurIPS. 10271--10282.  Kiran K Thekumparampil Ashish Khetan Zinan Lin and Sewoong Oh. 2018. Robustness of conditional GANs to noisy labels. In NeurIPS. 10271--10282."},{"key":"e_1_3_2_1_54_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision. 322--330","author":"Ma Xingjun","year":"2019","unstructured":"YisenWang, Xingjun Ma , Zaiyi Chen , Yuan Luo , Jinfeng Yi , and James Bailey . 2019 . Symmetric cross entropy for robust learning with noisy labels . In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 322--330 . YisenWang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. 2019. Symmetric cross entropy for robust learning with noisy labels. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 322--330."},{"key":"e_1_3_2_1_55_1","volume-title":"Implicit semantic data augmentation for deep networks. Advances in Neural Information Processing Systems 32","author":"Wang Yulin","year":"2019","unstructured":"Yulin Wang , Xuran Pan , Shiji Song , Hong Zhang , Gao Huang , and Cheng Wu. 2019. Implicit semantic data augmentation for deep networks. Advances in Neural Information Processing Systems 32 ( 2019 ). Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, and Cheng Wu. 2019. Implicit semantic data augmentation for deep networks. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_56_1","unstructured":"Hongxin Wei Lei Feng Xiangyu Chen and Bo An. 2020. Combating noisy labels by agreement: A joint training method with co-regularization. In CVPR. 13726--13735.  Hongxin Wei Lei Feng Xiangyu Chen and Bo An. 2020. Combating noisy labels by agreement: A joint training method with co-regularization. In CVPR. 13726--13735."},{"key":"e_1_3_2_1_57_1","unstructured":"Jiaheng Wei and Yang Liu. 2021. When Optimizing ?? -divergence is Robust with Label Noise. In ICLR.  Jiaheng Wei and Yang Liu. 2021. When Optimizing ?? -divergence is Robust with Label Noise. In ICLR."},{"key":"e_1_3_2_1_58_1","unstructured":"SonghuaWu Xiaobo Xia Tongliang Liu Bo Han Mingming Gong NannanWang Haifeng Liu and Gang Niu. 2021. Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. In ICML.  SonghuaWu Xiaobo Xia Tongliang Liu Bo Han Mingming Gong NannanWang Haifeng Liu and Gang Niu. 2021. Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. In ICML."},{"key":"e_1_3_2_1_59_1","volume-title":"Ngc: A unified framework for learning with open-world noisy data. In ICCV. 62--71.","author":"Jiang Jianwen","year":"2021","unstructured":"Zhi-FanWu, TongWei, Jianwen Jiang , Chaojie Mao , Mingqian Tang , and Yu-Feng Li . 2021 . Ngc: A unified framework for learning with open-world noisy data. In ICCV. 62--71. Zhi-FanWu, TongWei, Jianwen Jiang, Chaojie Mao, Mingqian Tang, and Yu-Feng Li. 2021. Ngc: A unified framework for learning with open-world noisy data. In ICCV. 62--71."},{"key":"e_1_3_2_1_60_1","volume-title":"Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels","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 ( 2022 ). 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 (2022)."},{"key":"e_1_3_2_1_61_1","unstructured":"Xiaobo Xia Tongliang Liu Bo Han Chen Gong Nannan Wang Zongyuan Ge and Yi Chang. 2021. Robust early-learning: Hindering the memorization of noisy labels. In ICLR.  Xiaobo Xia Tongliang Liu Bo Han Chen Gong Nannan Wang Zongyuan Ge and Yi Chang. 2021. Robust early-learning: Hindering the memorization of noisy labels. In ICLR."},{"key":"e_1_3_2_1_62_1","unstructured":"Xiaobo Xia Tongliang Liu Bo Han Mingming Gong Jun Yu Gang Niu and Masashi Sugiyama. 2022. Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. In ICLR.  Xiaobo Xia Tongliang Liu Bo Han Mingming Gong Jun Yu Gang Niu and Masashi Sugiyama. 2022. Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. In ICLR."},{"key":"e_1_3_2_1_63_1","unstructured":"Xiaobo Xia Tongliang Liu Bo Han Nannan Wang Mingming Gong Haifeng Liu Gang Niu Dacheng Tao and Masashi Sugiyama. 2020. Part-dependent label noise: Towards instance-dependent label noise. In NeurIPS.  Xiaobo Xia Tongliang Liu Bo Han Nannan Wang Mingming Gong Haifeng Liu Gang Niu Dacheng Tao and Masashi Sugiyama. 2020. Part-dependent label noise: Towards instance-dependent label noise. In NeurIPS."},{"key":"e_1_3_2_1_64_1","unstructured":"Xiaobo Xia Tongliang Liu Nannan Wang Bo Han Chen Gong Gang Niu and Masashi Sugiyama. 2019. Are Anchor Points Really Indispensable in Label-Noise Learning?. In NeurIPS. 6835--6846.  Xiaobo Xia Tongliang Liu Nannan Wang Bo Han Chen Gong Gang Niu and Masashi Sugiyama. 2019. Are Anchor Points Really Indispensable in Label-Noise Learning?. In NeurIPS. 6835--6846."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"crossref","unstructured":"Tong Xiao Tian Xia Yi Yang Chang Huang and XiaogangWang. 2015. Learning from massive noisy labeled data for image classification. In CVPR. 2691--2699.  Tong Xiao Tian Xia Yi Yang Chang Huang and XiaogangWang. 2015. Learning from massive noisy labeled data for image classification. In CVPR. 2691--2699.","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"e_1_3_2_1_66_1","unstructured":"Shuo Yang Lu Liu and Min Xu. 2021. Free Lunch for Few-shot Learning: Distribution Calibration. In ICLR.  Shuo Yang Lu Liu and Min Xu. 2021. Free Lunch for Few-shot Learning: Distribution Calibration. In ICLR."},{"key":"e_1_3_2_1_67_1","unstructured":"Quanming Yao Hansi Yang Bo Han Gang Niu and James Tin-Yau Kwok. 2020. Searching to exploit memorization effect in learning with noisy labels. In ICML. 10789--10798.  Quanming Yao Hansi Yang Bo Han Gang Niu and James Tin-Yau Kwok. 2020. Searching to exploit memorization effect in learning with noisy labels. In ICML. 10789--10798."},{"key":"e_1_3_2_1_68_1","volume-title":"Jo-src: A contrastive approach for combating noisy labels. In CVPR. 5192--5201.","author":"Yao Yazhou","year":"2021","unstructured":"Yazhou Yao , Zeren Sun , Chuanyi Zhang , Fumin Shen , Qi Wu , Jian Zhang , and Zhenmin Tang . 2021 . Jo-src: A contrastive approach for combating noisy labels. In CVPR. 5192--5201. Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, and Zhenmin Tang. 2021. Jo-src: A contrastive approach for combating noisy labels. In CVPR. 5192--5201."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"crossref","unstructured":"Kun Yi and Jianxin Wu. 2019. Probabilistic end-to-end noise correction for learning with noisy labels. In CVPR. 7017--7025.  Kun Yi and Jianxin Wu. 2019. Probabilistic end-to-end noise correction for learning with noisy labels. In CVPR. 7017--7025.","DOI":"10.1109\/CVPR.2019.00718"},{"key":"e_1_3_2_1_70_1","unstructured":"Xingrui Yu Bo Han Jiangchao Yao Gang Niu Ivor W Tsang and Masashi Sugiyama. 2019. How Does Disagreement Benefit Co-teaching?. In ICML.  Xingrui Yu Bo Han Jiangchao Yao Gang Niu Ivor W Tsang and Masashi Sugiyama. 2019. How Does Disagreement Benefit Co-teaching?. In ICML."},{"key":"e_1_3_2_1_71_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis . 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 ( 2016 ). Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_1_72_1","unstructured":"Yivan Zhang Gang Niu and Masashi Sugiyama. 2021. Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. In ICML.  Yivan Zhang Gang Niu and Masashi Sugiyama. 2021. Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. In ICML."},{"key":"e_1_3_2_1_73_1","unstructured":"Yikai Zhang Songzhu Zheng PengxiangWu Mayank Goswami and Chao Chen. 2021. Learning with Feature-Dependent Label Noise: A Progressive Approach. In ICLR.  Yikai Zhang Songzhu Zheng PengxiangWu Mayank Goswami and Chao Chen. 2021. Learning with Feature-Dependent Label Noise: A Progressive Approach. In ICLR."},{"key":"e_1_3_2_1_74_1","unstructured":"Zhilu Zhang and Mert Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. In NeurIPS. 8778--8788.  Zhilu Zhang and Mert Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. In NeurIPS. 8778--8788."},{"key":"e_1_3_2_1_75_1","unstructured":"Songzhu Zheng Pengxiang Wu Aman Goswami Mayank Goswami Dimitris Metaxas and Chao Chen. 2020. Error-bounded correction of noisy labels. In ICML. 11447--11457.  Songzhu Zheng Pengxiang Wu Aman Goswami Mayank Goswami Dimitris Metaxas and Chao Chen. 2020. Error-bounded correction of noisy labels. In ICML. 11447--11457."},{"key":"e_1_3_2_1_76_1","unstructured":"Zhaowei Zhu Tongliang Liu and Yang Liu. 2021. A second-order approach to learning with instance-dependent label noise. In CVPR. 10113--10123.  Zhaowei Zhu Tongliang Liu and Yang Liu. 2021. A second-order approach to learning with instance-dependent label noise. In CVPR. 10113--10123."}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Lisboa Portugal","acronym":"MM '22"},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547984","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547984","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:31Z","timestamp":1750186831000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":75,"alternative-id":["10.1145\/3503161.3547984","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547984","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}