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Existing research on learning with noisy labels mainly focuses on synthetic label noise.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Collaborative active learning of a kernel machine ensemble for recognition
Gang Hua, Chengjiang Long, Ming Yang, and Yan Gao · 2013
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
Earlier work this paper cites.
Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
Earlier work this paper cites.
Multi-class multi-annotator active learning with robust gaussian process for visual recognition
Chengjiang Long and Gang Hua · 2015
Earlier work this paper cites.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
Earlier work this paper cites.
Webvision database: Visual learning and understanding from web data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
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, and Masashi Sugiyama · 2018
Earlier work this paper cites.
Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
Cited alongside, same era.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
Cited alongside, same era.
Robust bi-tempered logistic loss based on bregman divergences
Ehsan Amid, Manfred KK Warmuth, Rohan Anil, and Tomer Koren · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
Does label smoothing mitigate label noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, and Sanjiv Kumar · 2020
Later among the works it cites.
Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An · 2020
Later among the works it cites.
When optimizing f f -divergence is robust with label noise
Jiaheng Wei and Yang Liu · 2020
Later among the works it cites.
Dual T: Reducing estimation error for transition matrix in label-noise learning
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, and Masashi Sugiyama · 2020
Later among the works it cites.
Understanding and improving early stopping for learning with noisy labels
Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, and Tongliang Liu · 2021
Closest in time.
Learning with instance-dependent label noise: A sample sieve approach
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David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Cited alongside, same era.
Human uncertainty makes classification more robust
Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky · 2019
Cited alongside, same era.
Selfie: Refurbishing unclean samples for robust deep learning
Hwanjun Song, Minseok Kim, and Jae-Gil Lee · 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.
Are anchor points really indispensable in label-noise learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama · 2019
Cited alongside, same era.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama · 2019
Cited alongside, same era.
Beyond synthetic noise: Deep learning on controlled noisy labels
Lu Jiang, Di Huang, Mason Liu, and Weilong Yang · 2020
Cited alongside, same era.
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2021
Closest in time.
Provably end-to-end label-noise learning without anchor points
Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, and Masashi Sugiyama · 2021
Closest in time.
Towards good practices for efficiently annotating large-scale image classification datasets
Yuan-Hong Liao, Amlan Kar, and Sanja Fidler · 2021
Closest in time.
Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2021
Closest in time.
Understanding (generalized) label smoothing whenlearning with noisy labels
Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, and Yang Liu · 2021
Closest in time.
Artificial neural variability for deep learning: on overfitting, noise memorization, and catastrophic forgetting
Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, and Masashi Sugiyama · 2021
Closest in time.
An information fusion approach to learning with instance-dependent label noise
Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen, Soo-Hyun Choi, and Xia Hu · 2022
Closest in time.
Robust training under label noise by over-parameterization
Sheng Liu, Zhihui Zhu, Qing Qu, and Chong You · 2022
Closest in time.
The rich get richer: Disparate impact of semi-supervised learning
Zhaowei Zhu, Tianyi Luo, and Yang Liu · 2022
Closest in time.