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Noisy labels can impair the performance of deep neural networks.
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters
Markus Maier, Matthias Hein, and Ulrike Von Luxburg · 2009
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Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters
Markus Maier, Matthias Hein, and Ulrike Von Luxburg · 2009
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Rates of convergence for the cluster tree
Kamalika Chaudhuri and Sanjoy Dasgupta · 2010
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Computational topology: an introduction
Herbert Edelsbrunner and John Harer · 2010
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A study of the effect of different types of noise on the precision of supervised learning techniques
David F Nettleton, Albert Orriols-Puig, and Albert Fornells · 2010
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Rates of convergence for the cluster tree
Kamalika Chaudhuri and Sanjoy Dasgupta · 2010
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Learning to label aerial images from noisy data
Volodymyr Mnih and Geoffrey E. Hinton · 2012
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Persistence-based clustering in riemannian manifolds
Frédéric Chazal, Leonidas J Guibas, Steve Y Oudot, and Primoz Skraba · 2013
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Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 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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Learning from multiple annotators with varying expertise
Yan Yan, Rómer Rosales, Glenn Fung, Ramanathan Subramanian, and Jennifer Dy · 2014
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 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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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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On the resistance of nearest neighbor to random noisy labels
Wei Gao, Bin-Bin Yang, and Zhi-Hua Zhou · 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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A closer look at memorization in deep networks
Devansh Arpit, Stanislaw K. Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, and Simon Lacoste-Julien · 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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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
Cited alongside, same era.
Learning from noisy labels with distillation
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 2017
Cited alongside, same era.
Decoupling" when to update" from" how to update"
Eran Malach and Shai Shalev-Shwartz · 2017
Cited alongside, same era.
Composing tree graphical models with persistent homology features for clustering mixed-type data
Xiuyan Ni, Novi Quadrianto, Yusu Wang, and Chao Chen · 2017
Cited alongside, same era.
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.
Toward robustness against label noise in training deep discriminative neural networks
Unsupervised label noise modeling and loss correction
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2019
Later among the works it cites.
On symmetric losses for learning from corrupted labels
Nontawat Charoenphakdee, Jongyeong Lee, and Masashi Sugiyama · 2019
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A topological regularizer for classifiers via persistent homology
Chao Chen, Xiuyan Ni, Qinxun Bai, and Yusu Wang · 2019
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Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Benben Liao, Guangyong Chen, and Shengyu Zhang · 2019
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Connectivity-optimized representation learning via persistent homology
Christoph Hofer, Roland Kwitt, Marc Niethammer, and Mandar Dixit · 2019
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Robust inference via generative classifiers for handling noisy labels
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Arash Vahdat · 2017
Cited alongside, same era.
Learning from noisy large-scale datasets with minimal supervision
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge J. Belongie · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
Cited alongside, same era.
Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate
Mikhail Belkin, Daniel J Hsu, and Partha Mitra · 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 W. Tsang, and Masashi Sugiyama · 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.
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin · 2019
Later among the works it cites.
Learning to learn from noisy labeled data
Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli · 2019
Later among the works it cites.
Rates of convergence for large-scale nearest neighbor classification
Xingye Qiao, Jiexin Duan, and Guang Cheng · 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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Combating label noise in deep learning using abstention
Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, and Jamal Mohd-Yusof · 2019
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Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
Later among the works it cites.
Probabilistic end-to-end noise correction for learning with noisy labels
Kun Yi and Jianxin Wu · 2019
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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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Dara Bahri, Heinrich Jiang, and Maya Gupta · 2020
Closest in time.
Simple and effective regularization methods for training on noisily labeled data with generalization guarantee
W Hu, Z Li, and D Yu · 2020
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Can gradient clipping mitigate label noise?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J Reddi, and Sanjiv Kumar · 2020
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Self: Learning to filter noisy labels with self-ensembling
Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, and Thomas Brox · 2020
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Persistence enhanced graph neural network
Qi Zhao, Ze Ye, Chao Chen, and Yusu Wang · 2020
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Error-bounded correction of noisy labels
Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, and Chao Chen · 2020
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