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Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy.
Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, and Ji Xu · 1903
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Special functions of mathematics for engineers , volume 49
Larry C Andrews · 1998
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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End-to-end text recognition with convolutional neural networks
Tao Wang, David J Wu, Adam Coates, and Andrew Y Ng · 2012
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
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Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
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Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks
Paras Lakhani and Baskaran Sundaram · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling
Sebastian Ramos, Stefan Gehrig, Peter Pinggera, Uwe Franke, and Carsten Rother · 2017
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Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner · 2018
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Pac-learning in the presence of adversaries
Daniel Cullina, Arjun Nitin Bhagoji, and Prateek Mittal · 2018
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Adversarial risk bounds for binary classification via function transformation
Justin Khim and Po-Ling Loh · 2018
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Adversarial examples for generative models
Jernej Kos, Ian Fischer, and Dawn Song · 2018
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Halo: Learning semantics-aware representations for cross-lingual information extraction
Hongyuan Mei, Sheng Zhang, Kevin Duh, and Benjamin Van Durme · 2018
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Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine
Apapan Pumsirirat and Liu Yan · 2018
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Adversarially robust generalization requires more data
Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever · 2019
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Adversarial training can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C Duchi, and Percy Liang · 2019
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Image synthesis with a single (robust) classifier
Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Improving the generalization of adversarial training with domain adaptation
Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft · 2019
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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Improved sample complexities for deep networks and robust classification via an all-layer margin
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Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
Cited alongside, same era.
Lower bounds on adversarial robustness from optimal transport
Arjun Nitin Bhagoji, Daniel Cullina, and Prateek Mittal · 2019
Cited alongside, same era.
Adversarial examples from computational constraints
Sebastien Bubeck, Yin Tat Lee, Eric Price, and Ilya Razenshteyn · 2019
Cited alongside, same era.
Lower bounds for adversarially robust pac learning
Dimitrios I Diochnos, Saeed Mahloujifar, and Mohammad Mahmoody · 2019
Cited alongside, same era.
Generalized no free lunch theorem for adversarial robustness
Elvis Dohmatob · 2019
Cited alongside, same era.
Convergence of adversarial training in overparametrized neural networks
Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, and Jason D Lee · 2019
Cited alongside, same era.
Colin Wei and Tengyu Ma · 2019
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Denxfpn: Pulmonary pathologies detection based on dense feature pyramid networks
Jun Xiao, Yuanxing Zhang, Kaigui Bian, Guopeng Zhou, and Wei Yan · 2019
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Rademacher complexity for adversarially robust generalization
Dong Yin, Ramchandran Kannan, and Peter Bartlett · 2019
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Adversarially robust generalization just requires more unlabeled data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, and Liwei Wang · 2019
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Interpreting adversarially trained convolutional neural networks
Tianyuan Zhang and Zhanxing Zhu · 2019
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More data can expand the generalization gap between adversarially robust and standard models
Lin Chen, Yifei Min, Mingrui Zhang, and Amin Karbasi · 2020
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Precise tradeoffs in adversarial training for linear regression
Adel Javanmard, Mahdi Soltanolkotabi, and Hamed Hassani · 2020
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Shangbang Long, Yushuo Guan, Kaigui Bian, and Cong Yao · 2020
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Efficiently learning adversarially robust halfspaces with noise
Omar Montasser, Surbhi Goel, Ilias Diakonikolas, and Nathan Srebro · 2020
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