{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:43:48Z","timestamp":1783953828932,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"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":[[2021,10,17]]},"DOI":"10.1145\/3474085.3475622","type":"proceedings-article","created":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T06:09:05Z","timestamp":1634537345000},"page":"1405-1413","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":121,"title":["Co-learning"],"prefix":"10.1145","author":[{"given":"Cheng","family":"Tan","sequence":"first","affiliation":[{"name":"Westlake University &amp; Westlake Institute for Advanced Study, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Xia","sequence":"additional","affiliation":[{"name":"Westlake University &amp; Westlake Institute for Advanced Study, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lirong","family":"Wu","sequence":"additional","affiliation":[{"name":"Westlake University &amp; Westlake Institute for Advanced Study, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stan Z.","family":"Li","sequence":"additional","affiliation":[{"name":"Westlake University &amp; Westlake Institute for Advanced Study, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"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 Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, 312-- 321 . http:\/\/proceedings.mlr.press\/v97\/arazo19a.html Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, and Kevin Mcguinness. 2019. Unsupervised Label Noise Modeling and Loss Correction. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, 312--321. http:\/\/proceedings.mlr.press\/v97\/arazo19a.html"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454741"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"e_1_3_2_2_4_1","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Caron Mathilde","year":"2020","unstructured":"Mathilde Caron , Ishan Misra , Julien Mairal , Priya Goyal , Piotr Bojanowski , and Armand Joulin . 2020 . Unsupervised learning of visual features by contrasting cluster assignments . Advances in Neural Information Processing Systems , Vol. 33 (2020). Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, Vol. 33 (2020)."},{"key":"e_1_3_2_2_5_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"1070","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 Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, 1062-- 1070 . http:\/\/proceedings.mlr.press\/v97\/chen19g.html Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang. 2019. Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, 1062--1070. http:\/\/proceedings.mlr.press\/v97\/chen19g.html"},{"key":"e_1_3_2_2_6_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research","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 Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 119), Hal Daum\u00e9 III and Aarti Singh (Eds.). PMLR, Vienna, 1597--1607. http:\/\/proceedings.mlr.press\/v119\/chen20j.html Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119), Hal Daum\u00e9 III and Aarti Singh (Eds.). PMLR, Vienna, 1597--1607. http:\/\/proceedings.mlr.press\/v119\/chen20j.html"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00138"},{"key":"e_1_3_2_2_9_1","volume-title":"International Conference on Learning Representations. ICLR","author":"Gidaris Spyros","year":"2018","unstructured":"Spyros Gidaris , Praveer Singh , and Nikos Komodakis . 2018 . Unsupervised Representation Learning by Predicting Image Rotations . In International Conference on Learning Representations. ICLR , Vancouver, 1--16. https:\/\/openreview.net\/forum?id=S1v4N2l0- Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018. Unsupervised Representation Learning by Predicting Image Rotations. In International Conference on Learning Representations. ICLR, Vancouver, 1--16. https:\/\/openreview.net\/forum?id=S1v4N2l0-"},{"key":"e_1_3_2_2_10_1","volume-title":"Lin (Eds.)","volume":"33","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill , Florian Strub , Florent Altch\u00e9 , Corentin Tallec , Pierre Richemond , Elena Buchatskaya , Carl Doersch , Bernardo Avila Pires , Zhaohan Guo , Mohammad Gheshlaghi Azar , Bilal Piot , koray kavukcuoglu, Remi Munos , and Michal Valko . 2020 . Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. In Advances in Neural Information Processing Systems,, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H . Lin (Eds.) , Vol. 33 . Curran Associates, Inc., Virtual, 21271--21284. https:\/\/proceedings.neurips.cc\/paper\/ 2020\/file\/f3ada80d5c4ee70142b17b8192b2958e-Paper.pdf Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko. 2020. Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. In Advances in Neural Information Processing Systems,, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., Virtual, 21271--21284. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/f3ada80d5c4ee70142b17b8192b2958e-Paper.pdf"},{"key":"e_1_3_2_2_11_1","volume-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels. In Advances in Neural Information Processing Systems","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. In Advances in Neural Information Processing Systems ,, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31 . Curran Associates, Inc. , Montreal . https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/a19744e268754fb0148b017647355b7b-Paper.pdf 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. In Advances in Neural Information Processing Systems,, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc., Montreal. https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/a19744e268754fb0148b017647355b7b-Paper.pdf"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00524"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327546.3327707"},{"key":"e_1_3_2_2_15_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"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 Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 80),, Jennifer Dy and Andreas Krause (Eds.). PMLR, Stockholm, 2304-- 2313 . http:\/\/proceedings.mlr.press\/v80\/jiang18c.html 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 Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80),, Jennifer Dy and Andreas Krause (Eds.). PMLR, Stockholm, 2304--2313. http:\/\/proceedings.mlr.press\/v80\/jiang18c.html"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00571"},{"key":"e_1_3_2_2_17_1","volume-title":"International Conference on Learning Representations. ICLR, New Orleans, 1--14","author":"Li Junnan","unstructured":"Junnan Li , Richard Socher , and Steven C.H. Hoi . 2020 a. DivideMix: Learning with Noisy Labels as Semi-supervised Learning . In International Conference on Learning Representations. ICLR, New Orleans, 1--14 . https:\/\/openreview.net\/forum?id=HJgExaVtwr Junnan Li, Richard Socher, and Steven C.H. Hoi. 2020 a. DivideMix: Learning with Noisy Labels as Semi-supervised Learning. In International Conference on Learning Representations. ICLR, New Orleans, 1--14. https:\/\/openreview.net\/forum?id=HJgExaVtwr"},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, 5051--5059","author":"Li Junnan","unstructured":"Junnan Li , Yongkang Wong , Qi Zhao , and Mohan S. Kankanhalli . 2019. Learning to Learn From Noisy Labeled Data . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, 5051--5059 . Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S. Kankanhalli. 2019. Learning to Learn From Noisy Labeled Data. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, 5051--5059."},{"key":"e_1_3_2_2_19_1","volume-title":"2020 b. Markov-Lipschitz deep learning. arXiv preprint","author":"Li Stan Z","year":"2020","unstructured":"Stan Z Li , Zelin Zang , and Lirong Wu . 2020 b. Markov-Lipschitz deep learning. arXiv preprint , Vol. arXiv: 2006 .08256 ( 2020 ), 1--10. Stan Z Li, Zelin Zang, and Lirong Wu. 2020 b. Markov-Lipschitz deep learning. arXiv preprint, Vol. arXiv:2006.08256 (2020), 1--10."},{"key":"e_1_3_2_2_20_1","volume-title":"2020 c. Deep Manifold Transformation for Dimension Reduction. arXiv preprint","author":"Li Stan Z","year":"2020","unstructured":"Stan Z Li , Zelin Zang , and Lirong Wu . 2020 c. Deep Manifold Transformation for Dimension Reduction. arXiv preprint , Vol. arXiv: 2010 .14831 ( 2020 ), 1--10. Stan Z Li, Zelin Zang, and Lirong Wu. 2020 c. Deep Manifold Transformation for Dimension Reduction. arXiv preprint, Vol. arXiv:2010.14831 (2020), 1--10."},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research","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 Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 119), Hal Daum\u00e9 III and Aarti Singh (Eds.). PMLR, Vienna, 6543--6553. http:\/\/proceedings.mlr.press\/v119\/ma20c.html Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey. 2020. Normalized Loss Functions for Deep Learning with Noisy Labels. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119), Hal Daum\u00e9 III and Aarti Singh (Eds.). PMLR, Vienna, 6543--6553. http:\/\/proceedings.mlr.press\/v119\/ma20c.html"},{"key":"e_1_3_2_2_22_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV). IEEE","author":"Mahajan Dhruv","unstructured":"Dhruv Mahajan , Ross Girshick , Vignesh Ramanathan , Kaiming He , Manohar Paluri , Yixuan Li , Ashwin Bharambe , and Laurens van der Maaten. 2018. Exploring the Limits of Weakly Supervised Pretraining . In Proceedings of the European Conference on Computer Vision (ECCV). IEEE , Munich, 181--196. Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens van der Maaten. 2018. Exploring the Limits of Weakly Supervised Pretraining. In Proceedings of the European Conference on Computer Vision (ECCV). IEEE, Munich, 181--196."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294863"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093342"},{"key":"e_1_3_2_2_25_1","volume-title":"International Conference on Learning Representations. ICLR, Virtual, 1--15","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 International Conference on Learning Representations. ICLR, Virtual, 1--15 . https:\/\/openreview.net\/forum?id=HkgsPhNYPS Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox. 2020SELF: Learning to Filter Noisy Labels with Self-Ensembling. In International Conference on Learning Representations. ICLR, Virtual, 1--15. https:\/\/openreview.net\/forum?id=HkgsPhNYPS"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"e_1_3_2_2_27_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 , Vol. 1807 , 03748 ( 2018 ), 1--13. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, Vol. 1807, 03748 (2018), 1--13."},{"key":"e_1_3_2_2_28_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Caesars Palace, 2536--2544","author":"Pathak Deepak","unstructured":"Deepak Pathak , Philipp Krahenbuhl , Jeff Donahue , Trevor Darrell , and Alexei A. Efros . 2016. Context Encoders: Feature Learning by Inpainting . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Caesars Palace, 2536--2544 . Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros. 2016. Context Encoders: Feature Learning by Inpainting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Caesars Palace, 2536--2544."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"e_1_3_2_2_30_1","volume-title":"ICLR (Workshop)","author":"Reed Scott E","unstructured":"Scott E Reed , Honglak Lee , Dragomir Anguelov , Christian Szegedy , Dumitru Erhan , and Andrew Rabinovich . 2015. Training Deep Neural Networks on Noisy Labels with Bootstrapping . In ICLR (Workshop) . ICLR , San Diego , 1--11. Scott E Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich. 2015. Training Deep Neural Networks on Noisy Labels with Bootstrapping. In ICLR (Workshop). ICLR, San Diego, 1--11."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454459"},{"key":"e_1_3_2_2_32_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"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 Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, 5907-- 5915 . http:\/\/proceedings.mlr.press\/v97\/song19b.html Hwanjun Song, Minseok Kim, and Jae-Gil Lee. 2019. SELFIE: Refurbishing Unclean Samples for Robust Deep Learning. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, 5907--5915. http:\/\/proceedings.mlr.press\/v97\/song19b.html"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01150"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2812802"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/3016100.3016212"},{"key":"e_1_3_2_2_39_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"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 Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Bench, 7164-- 7173 . http:\/\/proceedings.mlr.press\/v97\/yu19b.html Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama. 2019. How does Disagreement Help Generalization against Label Corruption?. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Bench, 7164--7173. http:\/\/proceedings.mlr.press\/v97\/yu19b.html"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"e_1_3_2_2_41_1","volume-title":"International Conference on Learning Representations. ICLR","author":"Zhang Hongyi","year":"2018","unstructured":"Hongyi Zhang , Moustapha Cisse , Yann N. Dauphin , and David Lopez-Paz . 2018 . mixup: Beyond Empirical Risk Minimization . In International Conference on Learning Representations. ICLR , Vancouver, 1--13. https:\/\/openreview.net\/forum?id=r1Ddp1-Rb Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2018. mixup: Beyond Empirical Risk Minimization. In International Conference on Learning Representations. ICLR, Vancouver, 1--13. https:\/\/openreview.net\/forum?id=r1Ddp1-Rb"},{"key":"e_1_3_2_2_42_1","volume-title":"Efros","author":"Zhang Richard","year":"2016","unstructured":"Richard Zhang , Phillip Isola , and Alexei A . Efros . 2016 . Colorful Image Colorization. In Computer Vision -- ECCV 2016,, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). Springer International Publishing , Cham, 649--666. Richard Zhang, Phillip Isola, and Alexei A. Efros. 2016. Colorful Image Colorization. In Computer Vision -- ECCV 2016,, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). Springer International Publishing, Cham, 649--666."},{"key":"e_1_3_2_2_43_1","volume-title":"Learning with Feature-Dependent Label Noise: A Progressive Approach. In International Conference on Learning Representations. ICLR, Virtual, 1--13","author":"Zhang Yikai","year":"2021","unstructured":"Yikai Zhang , Songzhu Zheng , Pengxiang Wu , Mayank Goswami , and Chao Chen . 2021 b . Learning with Feature-Dependent Label Noise: A Progressive Approach. In International Conference on Learning Representations. ICLR, Virtual, 1--13 . https:\/\/openreview.net\/forum?id=ZPa2SyGcbwh Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, and Chao Chen. 2021 b. Learning with Feature-Dependent Label Noise: A Progressive Approach. In International Conference on Learning Representations. ICLR, Virtual, 1--13. https:\/\/openreview.net\/forum?id=ZPa2SyGcbwh"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327546.3327555"}],"event":{"name":"MM '21: ACM Multimedia Conference","location":"Virtual Event China","acronym":"MM '21","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 29th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474085.3475622","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474085.3475622","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:24Z","timestamp":1750193304000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474085.3475622"}},"subtitle":["Learning from Noisy Labels with Self-supervision"],"short-title":[],"issued":{"date-parts":[[2021,10,17]]},"references-count":44,"alternative-id":["10.1145\/3474085.3475622","10.1145\/3474085"],"URL":"https:\/\/doi.org\/10.1145\/3474085.3475622","relation":{},"subject":[],"published":{"date-parts":[[2021,10,17]]},"assertion":[{"value":"2021-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}