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Noisy labels are inevitable in large real-world datasets.
Beyond synthetic noise: Deep learning on controlled noisy labels
Lu Jiang, Di Huang, Mason Liu, and Weilong Yang · 1911
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A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
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Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al · 1995
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Understanding generalization in deep learning via tensor methods
Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, and Furong Huang · 2001
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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2002
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Noise-tolerant learning, the parity problem, and the statistical query model
Avrim Blum, Adam Kalai, and Hal Wasserman · 2003
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Cheap and fast–but is it good? evaluating non-expert annotations for natural language tasks
Rion Snow, Brendan O’connor, Dan Jurafsky, and Andrew Y Ng · 2008
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2009
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The multidimensional wisdom of crowds
Peter Welinder, Steve Branson, Pietro Perona, and Serge J Belongie · 2010
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Noise resistant graph ranking for improved web image search
Wei Liu, Yu-Gang Jiang, Jiebo Luo, and Shih-Fu Chang · 2011
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Learning from multiple annotators with varying expertise
Yan Yan, Rómer Rosales, Glenn Fung, Ramanathan Subramanian, and Jennifer Dy · 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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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
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Webly supervised learning of convolutional networks
Xinlei Chen and Abhinav Gupta · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 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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Training deep neural-networks based on unreliable labels
Alan Joseph Bekker and Jacob Goldberger · 2016
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Learning deep networks from noisy labels with dropout regularization
Ishan Jindal, Matthew Nokleby, and Xuewen Chen · 2016
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Noise detection in the meta-learning level
Luís PF Garcia, André CPLF de Carvalho, and Ana C Lorena · 2016
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Robust semi-supervised learning through label aggregation
Yan Yan, Zhongwen Xu, Ivor W Tsang, Guodong Long, and Yi Yang · 2016
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep networks can resemble human feed-forward vision in invariant object recognition
Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh, and Timothée Masquelier · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 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
Cited alongside, same era.
Multiclass learning with partially corrupted labels
Ruxin Wang, Tongliang Liu, and Dacheng Tao · 2017
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Decoupling" when to update" from" how to update"
Eran Malach and Shai Shalev-Shwartz · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
J. Goldberger and E. Ben-Reuven · 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.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
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Learning from noisy labels by regularized estimation of annotator confusion
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C Alexander, and Nathan Silberman · 2019
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Learning to learn from noisy labeled data
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli · 2019
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S Du, Kangcheng Hou, Russ R Salakhutdinov, Barnabas Poczos, Ruosong Wang, and Keyulu Xu · 2019
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Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Cited alongside, same era.
Deep sets. corr abs/1703.06114 (2017)
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
Webvision database: Visual learning and understanding from web data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
Cited alongside, same era.
Learning with biased complementary labels
Xiyu Yu, Tongliang Liu, Mingming Gong, and Dacheng Tao · 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.
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.
Robust inference via generative classifiers for handling noisy labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin · 2019
Later among the works it cites.
The origins and prevalence of texture bias in convolutional neural networks
Katherine L Hermann, Ting Chen, and Simon Kornblith · 2019
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Approximation ratios of graph neural networks for combinatorial problems
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Deep self-learning from noisy labels
Jiangfan Han, Ping Luo, and Xiaogang Wang · 2019
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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
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Sigua: Forgetting may make learning with noisy labels more robust
Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W Tsang, and Masashi Sugiyama · 2020
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Early-learning regularization prevents memorization of noisy labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda · 2020
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What do neural networks learn when trained with random labels?
Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin, Robert JN Baldock, Olivier Bousquet, Sylvain Gelly, and Daniel Keysers · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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Deep k-nn for noisy labels
Dara Bahri, Heinrich Jiang, and Maya Gupta · 2020
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A topological filter for learning with label noise
Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, and Chao Chen · 2020
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What shapes feature representations? exploring datasets, architectures, and training
Katherine L Hermann and Andrew K Lampinen · 2020
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The pitfalls of simplicity bias in neural networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli · 2020
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How benign is benign overfitting?
Amartya Sanyal, Puneet K Dokania, Varun Kanade, and Philip HS Torr · 2020
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Interactive refinement of cross-lingual word embeddings
Michelle Yuan, Mozhi Zhang, Benjamin Van Durme, Leah Findlater, and Jordan Boyd-Graber · 2020
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Simple and effective regularization methods for training on noisily labeled data with generalization guarantee
Wei Hu, Zhiyuan Li, and Dingli Yu · 2020
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Learning with instance-dependent label noise: A sample sieve approach
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2020
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Proselflc: Progressive self label correction for target revising in label noise
Xinshao Wang, Yang Hua, Elyor Kodirov, and Neil M Robertson · 2020
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Denny Wu and Ji Xu · 2020
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Distilling effective supervision from severe label noise
Zizhao Zhang, Han Zhang, Sercan O Arik, Honglak Lee, and Tomas Pfister · 2020
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Optimization of graph neural networks: Implicit acceleration by skip connections and more depth
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, and Kenji Kawaguchi · 2021
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Information obfuscation of graph neural networks
Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey J Gordon, Stefanie Jegelka, and Ruslan Salakhutdinov · 2021
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Graphnorm: A principled approach to accelerating graph neural network training
Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-yan Liu, and Liwei Wang · 2021
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