Fetching the paper…
Reading the bibliography…
Machine learning classifiers have been demonstrated, both empirically and theoretically, to be robust to label noise under certain conditions -- notably the typical assumption is that label noise is independent of the features given the class label.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Rihamark: perceptual image hash benchmarking
Christoph Zauner, Martin Steinebach, and Eckehard Hermann · 2011
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.
Classification with asymmetric label noise: Consistency and maximal denoising
Clayton Scott, Gilles Blanchard, and Gregory Handy · 2013
Earlier work this paper cites.
Decontamination of Mutually Contaminated Models
Gilles Blanchard and Clayton Scott · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
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 · 2015
Earlier work this paper cites.
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Clayton Scott · 2015
Earlier work this paper cites.
Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
Cited alongside, same era.
Learning summary statistic for approximate bayesian computation via deep neural network
Bai Jiang, Tung-yu Wu, Charles Zheng, and Wing H Wong · 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
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.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 2019
Later among the works it cites.
On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
Later among the works it cites.
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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 2017
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.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
ℒ D M I \mathcal{L}_{DMI} : A novel information-theoretic loss function for training deep nets robust to label noise
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
Later among the works it cites.
Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2020
Later among the works it cites.
Learning soft labels via meta learning
Nidhi Vyas, Shreyas Saxena, and Thomas Voice · 2020
Later among the works it cites.
Confident learning: Estimating uncertainty in dataset labels
Curtis Northcutt, Lu Jiang, and Isaac Chuang · 2021
Later among the works it cites.
VisHash: Visual similarity preserving image hashing for diagram retrieval
Diane Oyen, Michal Kucer, and Brendt Wohlberg · 2021
Later among the works it cites.