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Learning from noisy labels is an important and long-standing problem in machine learning for real applications.
Early stopping-but when?
Lutz Prechelt · 2002
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Pattern recognition and machine learning
Christopher M Bishop and Nasser M Nasrabadi · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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On the theory of stochastic processes, with particular reference to applications
William Feller · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry · 2017
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Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
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Webvision database: Visual learning and understanding from web data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
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Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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Iterative learning with open-set noisy labels
Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
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Learning to learn from noisy labeled data
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli · 2019
Cited alongside, same era.
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
Cited alongside, same era.
Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
Cited alongside, same era.
Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
Cited alongside, same era.
Unsupervised label noise modeling and loss correction
Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, and Kevin McGuinness · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Confidence scores make instance-dependent label-noise learning possible
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 2021
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Learning with feature-dependent label noise: A progressive approach
Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, and Chao Chen · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Instance-dependent label-noise learning with manifold-regularized transition matrix estimation
De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, and Masashi Sugiyama · 2022
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Cited alongside, same era.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama · 2019
Cited alongside, same era.
Deep self-learning from noisy labels
Jiangfan Han, Ping Luo, and Xiaogang Wang · 2019
Cited alongside, same era.
Part-dependent label noise: Towards instance-dependent label noise
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, and Masashi Sugiyama · 2020
Cited alongside, same era.
Learning with bounded instance and label-dependent label noise
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, and Dacheng Tao · 2020
Cited alongside, same era.
Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 2020
Cited alongside, same era.
Error-bounded correction of noisy labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen · 2020
Cited alongside, same era.
Fair classification with instance-dependent label noise
Songhua Wu, Mingming Gong, Bo Han, Yang Liu, and Tongliang Liu · 2022
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Learning with neighbor consistency for noisy labels
Ahmet Iscen, Jack Valmadre, Anurag Arnab, and Cordelia Schmid · 2022
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Neighbour consistency guided pseudo-label refinement for unsupervised person re-identification
De Cheng, Haichun Tai, Nannan Wang, Zhen Wang, and Xinbo Gao · 2022
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Is self-supervised learning more robust than supervised learning?
Yuanyi Zhong, Haoran Tang, Junkun Chen, Jian Peng, and Yu-Xiong Wang · 2022
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Card: Classification and regression diffusion models
Xizewen Han, Huangjie Zheng, and Mingyuan Zhou · 2022
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Semi-parametric neural image synthesis
Andreas Blattmann, Robin Rombach, Kaan Oktay, Jonas Müller, and Björn Ommer · 2022
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Retrieval augmented classification for long-tail visual recognition
Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, and Anton van den Hengel · 2022
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Centrality and consistency: two-stage clean samples identification for learning with instance-dependent noisy labels
Ganlong Zhao, Guanbin Li, Yipeng Qin, Feng Liu, and Yizhou Yu · 2022
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Contrast to divide: Self-supervised pre-training for learning with noisy labels
Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Unicon: Combating label noise through uniform selection and contrastive learning
Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard, Ajmal Mian, and Mubarak Shah · 2022
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Longremix: Robust learning with high confidence samples in a noisy label environment
Filipe R Cordeiro, Ragav Sachdeva, Vasileios Belagiannis, Ian Reid, and Gustavo Carneiro · 2023
Closest in time.
Star-shaped denoising diffusion probabilistic models
Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Aibek Alanov, and Dmitry Vetrov · 2023
Closest in time.
Efficient utilization of pre-trained model for learning with noisy labels
Jongwoo Ko, Sumyeong Ahn, and Se-Young Yun · 2023
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Adaptive sample selection for robust learning under label noise
Deep Patel and PS Sastry · 2023
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Learning with noisy labels via self-supervised adversarial noisy masking
Yuanpeng Tu, Boshen Zhang, Yuxi Li, Liang Liu, Jian Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, and Cai Rong Zhao · 2023
Closest in time.