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In supervised learning, it has been shown that label noise in the data can be interpolated without penalties on test accuracy.
Learning from noisy examples
Dana Angluin and Philip Laird · 1988
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Adaptive estimation of a quadratic functional by model selection
B. Laurent and P. Massart · 2000
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The PASCAL Visual Object Classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Support vector machines under adversarial label noise
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2011
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
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Adversarial examples in the physical world
A Kurakin, I Goodfellow, and S Bengio · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
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Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
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A general characterization of the statistical query complexity
Vitaly Feldman · 2017
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Multiscale sequence modeling with a learned dictionary
Bart van Merriënboer, Amartya Sanyal, Hugo Larochelle, and Yoshua Bengio · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Battista Biggio and Fabio Roli · 2018
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Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
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Neural anisotropy directions
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi, and Pascal Frossard · 2020
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The pitfalls of simplicity bias in neural networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli · 2020
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A universal law of robustness via isoperimetry
Sebastien Bubeck and Mark Sellke · 2021
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Fundamental tradeoffs between memorization and robustness in random features and neural tangent regimes, 2021
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Analysing the noise model error for realistic noisy label data
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How benign is benign overfitting?
Amartya Sanyal, Puneet K. Dokania, Varun Kanade, and Philip Torr · 2021
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Ensemble adversarial training: Attacks and defenses
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The dimpled manifold model of adversarial examples in machine learning
Adi Shamir, Odelia Melamed, and Oriel BenShmuel · 2021
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Understanding the interaction of adversarial training with noisy labels
Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankanhalli, and Masashi Sugiyama · 2021
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Fast rates for noisy interpolation require rethinking the effects of inductive bias
Konstantin Donhauser, Nicolo Ruggeri, Stefan Stojanovic, and Fanny Yang · 2022
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Surprises in high-dimensional ridgeless least squares interpolation
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Indiscriminate data poisoning attacks on neural networks, 2022
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Catastrophic overfitting is a bug but also a feature
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Learning with noisy labels revisited: A study using real-world human annotations
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