Fetching the paper…
Reading the bibliography…
Recent studies on the memorization effects of deep neural networks on noisy labels show that the networks first fit the correctly-labeled training samples before memorizing the mislabeled samples.
Xinshao Wang, Yang Hua, Elyor Kodirov, and Neil M Robertson · 1903
Earlier work this paper cites.
Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 1908
Earlier work this paper cites.
Learning from noisy labels with distillation
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 1918
Earlier work this paper cites.
Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2002
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
Earlier work this paper cites.
Learning deconvolution network for semantic segmentation
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han · 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.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Earlier work this paper cites.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzebski, 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.
Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 2017
Earlier work this paper cites.
Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie · 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.
Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
Earlier work this paper cites.
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 · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
Learning with biased complementary labels
Xiyu Yu, Tongliang Liu, Mingming Gong, and Dacheng Tao · 2018
Probabilistic end-to-end noise correction for learning with noisy labels
Kun Yi and Jianxin Wu · 2019
Later among the works it cites.
Unsupervised label noise modeling and loss correction
Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, and Kevin Mcguinness · 2019
Later among the works it cites.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Later among the works it cites.
Simple and effective regularization methods for training on noisily labeled data with generalization guarantee
Wei Hu, Zhiyuan Li, and Dingli Yu · 2019
Later among the works it cites.
Nlnl: Negative learning for noisy labels
Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim · 2019
Later among the works it cites.
Combating label noise in deep learning using abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, and Jamal Mohd-Yusof · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Understanding deep learning requires rethinking generalization, 2018
C Zhang, S Bengio, M Hardt, B Recht, and O Vinyals · 2018
Cited alongside, same era.
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.
Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 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.
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
Cited alongside, same era.
Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
Cited alongside, same era.
Later among the works it cites.
Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang · 2019
Later among the works it cites.
Prestopping: How does early stopping help generalization against label noise?
Hwanjun Song, Minseok Kim, Dongmin Park, and Jae-Gil Lee · 2019
Later among the works it cites.
Early-learning regularization prevents memorization of noisy labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda · 2020
Later among the works it cites.
Learning with bounded instance and label-dependent label noise
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, and Dacheng Tao · 2020
Later among the works it cites.
Parts-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
Later among the works it cites.
Normalized loss functions for deep learning with noisy labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey · 2020
Later among the works it cites.
Can cross entropy loss be robust to label noise
Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, and Bo An · 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.
Self-adaptive training: beyond empirical risk minimization
Lang Huang, Chao Zhang, and Hongyang Zhang · 2020
Later among the works it cites.
Self: learning to filter noisy labels with self-ensembling
Tam Nguyen, C Mummadi, T Ngo, L Beggel, and Thomas Brox · 2020
Later among the works it cites.
Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan R Elenberg, and Kilian Q Weinberger · 2020
Later among the works it cites.
Co-matching: Combating noisy labels by augmentation anchoring
Yangdi Lu, Yang Bo, and Wenbo He · 2021
Closest in time.