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Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Approximation with artificial neural networks
Balázs Csanád Csáji · 2001
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The foundations of cost-sensitive learning
Charles Elkan · 2001
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Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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A framework for robust subspace learning
De la Torre Fernando and J. Black Mkchael · 2003
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Learning and evaluating classifiers under sample selection bias
Bianca Zadrozny · 2004
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Cost-sensitive boosting for classification of imbalanced data
Yanmin Sun, Mohamed S Kamel, Andrew KC Wong, and Yang Wang · 2007
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Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
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Ensemble of exemplar-svms for object detection and beyond
Tomasz Malisiewicz, Abhinav Gupta, and Alexei A Efros · 2011
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A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
Mikel Galar, Alberto Fernandez, Edurne Barrenechea, Humberto Bustince, and Francisco Herrera · 2012
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Stochastic majorization-minimization algorithms for large-scale optimization
Julien Mairal · 2013
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Learning to predict from crowdsourced data
Wei Bi, Liwei Wang, James T Kwok, and Zhuowen Tu · 2014
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Easy samples first: Self-paced reranking for zero-example multimedia search
Lu Jiang, Deyu Meng, Teruko Mitamura, and Alexander G Hauptmann · 2014
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Self-paced learning with diversity
Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, and Alexander Hauptmann · 2014
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Learning from noisy labels with deep neural networks
Sainbayar Sukhbaatar and Rob Fergus · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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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 · 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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Auxiliary image regularization for deep cnns with noisy labels
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, and Trevor Darrell · 2016
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Learning to detect concepts from webly-labeled video data
Junwei Liang, Lu Jiang, Deyu Meng, and Alexander Hauptmann · 2016
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 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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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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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
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Sensitivity and generalization in neural networks: an empirical study
Roman Novak, Yasaman Bahri, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 2018
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Co-teaching: robust training 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
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Generalization in deep learning
Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio · 2017
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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
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Learning from noisy labels with distillation
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li · 2017
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Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 2017
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Attend in groups: a weakly-supervised deep learning framework for learning from web data
Bohan Zhuang, Lingqiao Liu, Yao Li, Chunhua Shen, and Ian D Reid · 2017
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Robust probabilistic modeling with bayesian data reweighting
Yixin Wang, Alp Kucukelbir, and David M Blei · 2017
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Zhilu Zhang and Mert R Sabuncu · 2018
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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
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Focal loss for dense object detection
Tsung-Yi Lin, Priyal Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2018
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Learning to teach with dynamic loss functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, and Tie-Yan Liu · 2018
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, and Massimilano Pontil · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
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Gradient agreement as an optimization objective for meta-learning
Amir Erfan Eshratifar, David Eigen, and Massoud Pedram · 2018
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Cost-sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, and Roberto Togneri · 2018
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Small sample learning in big data era
Jun Shu, Zongben Xu, and Deyu Meng · 2018
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Fidelity-weighted learning
Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, and Bernhard Schölkopf · 2018
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Learning to teach
Yang Fan, Fei Tian, Tao Qin, Xiang-Yang Li, and Tie-Yan Liu · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge Belongie · 2018
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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
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Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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Survey on deep learning with class imbalance
Justin M Johnson and Taghi M Khoshgoftaar · 2019
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Safeguarded dynamic label regression for noisy supervision
Jiangchao Yao, Hao Wu, Ya Zhang, Ivor W Tsang, and Jun Sun · 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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