Fetching the paper…
Reading the bibliography…
Label noise in multiclass classification is a major obstacle to the deployment of learning systems.
Learning from noisy examples
Dana Angluin and Philip Laird · 1988
Earlier work this paper cites.
Gradient-based learning applied to document recognition, 1998
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 1999
Earlier work this paper cites.
Learning object categories from Google’s image search
Robert Fergus, Li Fei-Fei, Pietro Perona, and Andrew Zisserman · 2005
Earlier work this paper cites.
Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
Earlier work this paper cites.
On the foundations of noise-free selective classification
Ran El-Yaniv et al · 2010
Earlier work this paper cites.
Random classification noise defeats all convex potential boosters
Philip M Long and Rocco A Servedio · 2010
Earlier work this paper cites.
Harvesting image databases from the web
Florian Schroff, Antonio Criminisi, and Andrew Zisserman · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning, 2011
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 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.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
Earlier work this paper cites.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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 E Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
Earlier work this paper cites.
Learning with symmetric label noise: The importance of being unhinged
Brendan Van Rooyen, Aditya Menon, and Robert C Williamson · 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.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2017
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
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
Decoupling “when to update” from “how to update”
Eran Malach and Shai Shalev-Shwartz · 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.
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, et al · 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
Later among the works it cites.
Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman · 2019
Later among the works it cites.
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
Later among the works it cites.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama · 2019
Later among the works it cites.
Learning with bounded instance- and label-dependent label noise
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
Cited alongside, same era.
Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 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.
Deep learning for classical japanese literature
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, and David Ha · 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.
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.
Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, and Dacheng Tao · 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.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Later among the works it cites.
DivideMix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven C.H. Hoi · 2020
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.
Peer loss functions: Learning from noisy labels without knowing noise rates
Yang Liu and Hongyi Guo · 2020
Later among the works it cites.
Does label smoothing mitigate label noise?
Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, and Sanjiv Kumar · 2020
Later among the works it cites.
Curriculum loss: Robust learning and generalization against label corruption
Yueming Lyu and Ivor W. Tsang · 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 gradient clipping mitigate label noise?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J Reddi, and Sanjiv Kumar · 2020
Later among the works it cites.
Coresets for robust training of deep neural networks against noisy labels
Baharan Mirzasoleiman, Kaidi Cao, and Jure Leskovec · 2020
Later among the works it cites.
Consistent estimators for learning to defer to an expert
Hussein Mozannar and David Sontag · 2020
Later among the works it cites.
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
Later among the works it cites.
Intra order-preserving functions for calibration of multi-class neural networks
Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, and Byron Boots · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Later among the works it cites.
A topological filter for learning with label noise
Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, and Chao Chen · 2020
Later among the works it cites.
Part-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.
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.
Beyond class-conditional assumption: A primary attempt to combat instance-dependent label noise
Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, and Pheng-Ann Heng · 2021
Closest in time.