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Deep neural networks (DNNs) have great expressive power, which can even memorize samples with wrong labels.
Convexity, classification, and risk bounds
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Robust truncated hinge loss support vector machines
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On the design of loss functions for classification: theory, robustness to outliers, and savageboost
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Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
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L1 and l2 regularization for multiclass hinge loss models
Robert Moore and John DeNero · 2011
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Crowdsourcing annotations for visual object detection
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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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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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Self-paced curriculum learning
Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander G Hauptmann · 2015
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Learning with symmetric label noise: The importance of being unhinged
Brendan Van Rooyen, Aditya Menon, and Robert C Williamson · 2015
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Virtual adversarial training for semi-supervised text classification
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A closer look at memorization in deep networks
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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
Does distributionally robust supervised learning give robust classifiers?
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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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Learning from noisy singly-labeled data
Ashish Khetan, Zachary C Lipton, and Anima Anandkumar · 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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Generalized cross entropy loss for training deep neural networks with noisy labels
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Robust web image annotation via exploring multi-facet and structural knowledge
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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
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Understanding deep learning requires rethinking generalization
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Masking: A new perspective of noisy supervision
Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, and Masashi Sugiyama
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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
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Zhilu Zhang and Mert Sabuncu · 2018
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Robust inference via generative classifiers for handling noisy labels
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How does disagreement help generalization against label corruption?
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Marginalized average attentional network for weakly-supervised learning
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