Fetching the paper…
Reading the bibliography…
Real-world large-scale datasets usually contain noisy labels and are imbalanced.
Xinshao Wang, Yang Hua, Elyor Kodirov, and Neil M Robertson · 1903
Earlier work this paper cites.
Robust statistics
Peter J Huber · 1981
Earlier work this paper cites.
The approach based on influence functions
Frank R Hampel, Elvezio M Ronchetti, Peter J Rousseeuw, and WA Stahel · 1986
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Statistical learning with sparsity: the lasso and generalizations
Trevor Hastie, Robert Tibshirani, and Martin Wainwright · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Self-paced curriculum learning
Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander G Hauptmann · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews
Grégoire Mesnil, Tomas Mikolov, Marc’Aurelio Ranzato, and Yoshua Bengio · 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.
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.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 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
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Cited alongside, same era.
Adaptive sampling for sgd by exploiting side information
Siddharth Gopal · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
On loss functions for deep neural networks in classification
Katarzyna Janocha and Wojciech Marian Czarnecki · 2016
Cited alongside, same era.
Online batch selection for faster training of neural networks
Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
Video person re-identification with competitive snippet-similarity aggregation and co-attentive snippet embedding
Dapeng Chen, Hongsheng Li, Tong Xiao, Shuai Yi, and Xiaogang Wang · 2018
Later among the works it 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 · 2018
Later among the works it cites.
Diversity regularized spatiotemporal attention for video-based person re-identification
Shuang Li, Slawomir Bak, Peter Carr, and Xiaogang Wang · 2018
Later among the works it cites.
Dimensionality-driven learning with noisy labels
Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah M Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, and James Bailey · 2018
Later among the works it cites.
Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
Cited alongside, same era.
Mars: A video benchmark for large-scale person re-identification
Liang Zheng, Zhi Bie, Yifan Sun, Jingdong Wang, Chi Su, Shengjin Wang, and Qi Tian · 2016
Cited alongside, same era.
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, and Simon Lacoste-Julien · 2017
Cited alongside, same era.
Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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.
Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
Later among the works it cites.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R Sabuncu · 2018
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
Closest in time.
A general and adaptive robust loss function
Jonathan T Barron · 2019
Closest in time.
On symmetric losses for learning from corrupted labels
Nontawat Charoenphakdee, Jongyeong Lee, and Masashi Sugiyama · 2019
Closest in time.
Nlnl: Negative learning for noisy labels
Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim · 2019
Closest in time.
Robust inference via generative classifiers for handling noisy labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin · 2019
Closest in time.
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
Closest in time.
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
Closest in time.
Beyond synthetic noise: Deep learning on controlled noisy labels
Lu Jiang, Di Huang, Mason Liu, and Weilong Yang · 2020
Closest in time.
Normalized loss functions for deep learning with noisy labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano Sarah Erfani, and James Bailey · 2020
Closest in time.
Proselflc: Progressive self label correction for training robust deep neural networks
Xinshao Wang, Yang Hua, Elyor Kodirov, and Neil M Robertson · 2020
Closest in time.
Searching to exploit memorization effect in learning with noisy labels
Quanming Yao, Hansi Yang, Bo Han, Gang Niu, and J Kwok · 2020
Closest in time.