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Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed.
Improved sample complexities for deep networks and robust classification via an all-layer margin
Colin Wei and Tengyu Ma · 1910
Earlier work this paper cites.
Using qualitative hypotheses to identify inaccurate data
Qi Zhao and Toyoaki Nishida · 1995
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Instance pruning techniques
D Randall Wilson and Tony R Martinez · 1997
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Identifying mislabeled training data
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Spatially adaptive smoothing splines
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Learning word vectors for sentiment analysis
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Classification in the presence of label noise: a survey
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Local and global asymptotic inference in smoothing spline models
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Adam: A method for stochastic optimization
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Classification with noisy labels by importance reweighting
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Deep residual learning for image recognition
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Learning from binary labels with instance-dependent corruption
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Understanding deep learning requires rethinking generalization
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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 with bounded instance-and label-dependent label noise
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Robust loss functions under label noise for deep neural networks
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Understanding and utilizing deep neural networks trained with noisy labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang · 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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On the power of curriculum learning in training deep networks
Guy Hacohen and Daphna Weinshall · 2019
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Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 2019
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Making deep neural networks robust to label noise: A loss correction approach
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Robust large margin deep neural networks
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Learning from noisy large-scale datasets with minimal supervision
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Learning to model the tail
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Minimum" norm" neural networks are splines
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How do infinite width bounded norm networks look in function space?
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Meta-weight-net: Learning an explicit mapping for sample weighting
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Dynamic curriculum learning for imbalanced data classification
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L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
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Simple and effective regularization methods for training on noisily labeled data with generalization guarantee
W Hu, Z Li, and D Yu · 2020
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Coresets for robust training of neural networks against noisy labels
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Robust and on-the-fly dataset denoising for image classification
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