Contrastive Learning Improves Model Robustness Under Label Noise

19 April 2021

Papers citing "Contrastive Learning Improves Model Robustness Under Label Noise"

33 / 33 papers shown

Title
Multi-level Supervised Contrastive Learning Naghmeh Ghanooni Barbod Pajoum Harshit Rawal Sophie Fellenz Vo Nguyen Le Duy Marius Kloft 193 0 0 04 Feb 2025
Do We Really Need Gold Samples for Sample Weighting Under Label Noise? Aritra Ghosh Andrew Lan NoLa 70 9 0 19 Apr 2021
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels Evgenii Zheltonozhskii Chaim Baskin A. Mendelson A. Bronstein Or Litany SSL 97 94 0 25 Mar 2021
Early-Learning Regularization Prevents Memorization of Noisy Labels Sheng Liu Jonathan Niles-Weed N. Razavian C. Fernandez‐Granda NoLa 104 569 0 30 Jun 2020
Normalized Loss Functions for Deep Learning with Noisy Labels Xingjun Ma Hanxun Huang Yisen Wang Simone Romano S. Erfani James Bailey NoLa 76 445 0 24 Jun 2020
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Junnan Li R. Socher Guosheng Lin NoLa 110 1,034 0 18 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations Ting-Li Chen Simon Kornblith Mohammad Norouzi Geoffrey E. Hinton SSL 390 18,897 0 13 Feb 2020
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates Yang Liu Hongyi Guo NoLa 76 242 0 08 Oct 2019
L_DMI: An Information-theoretic Noise-robust Loss Function Yilun Xu Peng Cao Yuqing Kong Yizhou Wang NoLa 68 57 0 08 Sep 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks Mantas Mazeika Saurav Kadavath Basel Alomair OOD SSL 60 950 0 28 Jun 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot Nicholas Carlini Ian Goodfellow Nicolas Papernot Avital Oliver Colin Raffel 159 3,033 0 06 May 2019
Unsupervised Label Noise Modeling and Loss Correction Eric Arazo Sanchez Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 97 616 0 25 Apr 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 89 727 0 28 Jan 2019
Learning to Learn from Noisy Labeled Data Junnan Li Yongkang Wong Qi Zhao Mohan Kankanhalli NoLa 69 334 0 13 Dec 2018
Bilevel Programming for Hyperparameter Optimization and Meta-Learning Luca Franceschi P. Frasconi Saverio Salzo Riccardo Grazzi Massimiliano Pontil 179 732 0 13 Jun 2018
Masking: A New Perspective of Noisy Supervision Bo Han Jiangchao Yao Gang Niu Mingyuan Zhou Ivor Tsang Ya Zhang Masashi Sugiyama NoLa 78 255 0 21 May 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Zhilu Zhang M. Sabuncu NoLa 85 2,615 0 20 May 2018
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Bo Han Quanming Yao Xingrui Yu Gang Niu Miao Xu Weihua Hu Ivor Tsang Masashi Sugiyama NoLa 123 2,082 0 18 Apr 2018
Joint Optimization Framework for Learning with Noisy Labels Daiki Tanaka Daiki Ikami T. Yamasaki Kiyoharu Aizawa NoLa 74 712 0 30 Mar 2018
Learning to Reweight Examples for Robust Deep Learning Mengye Ren Wenyuan Zeng Binh Yang R. Urtasun OOD NoLa 152 1,431 0 24 Mar 2018
Robust Loss Functions under Label Noise for Deep Neural Networks Aritra Ghosh Himanshu Kumar P. Sastry NoLa OOD 80 959 0 27 Dec 2017
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels Lu Jiang Zhengyuan Zhou Thomas Leung Li Li Li Fei-Fei NoLa 131 1,456 0 14 Dec 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 316 9,811 0 25 Oct 2017
Large Batch Training of Convolutional Networks Yang You Igor Gitman Boris Ginsburg ODL 141 852 0 13 Aug 2017
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets P. Chrabaszcz I. Loshchilov Frank Hutter SSeg OOD 173 649 0 27 Jul 2017
Learning From Noisy Large-Scale Datasets With Minimal Supervision Andreas Veit N. Alldrin Gal Chechik Ivan Krasin Abhinav Gupta Serge J. Belongie 142 480 0 06 Jan 2017
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Zhuang Li NoLa 120 1,459 0 13 Sep 2016
Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen A. Menon Robert C. Williamson NoLa 172 313 0 28 May 2015
Webly Supervised Learning of Convolutional Networks Xinlei Chen Abhinav Gupta SSL 90 373 0 07 May 2015
Training Deep Neural Networks on Noisy Labels with Bootstrapping Scott E. Reed Honglak Lee Dragomir Anguelov Christian Szegedy D. Erhan Andrew Rabinovich NoLa 125 1,023 0 20 Dec 2014
Making Risk Minimization Tolerant to Label Noise Aritra Ghosh Naresh Manwani P. Sastry NoLa 163 215 0 14 Mar 2014
Double Ramp Loss Based Reject Option Classifier Naresh Manwani Aritra Ghosh P. Sastry Ramasubramanian Sundararajan 84 49 0 26 Nov 2013
Identifying Mislabeled Training Data C. Brodley M. Friedl 111 972 0 01 Jun 2011