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Adversarial training is the industry standard for producing models that are robust to small adversarial perturbations.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M. Lopez · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Understanding batch normalization
Nils Bjorck, Carla P Gomes, Bart Selman, and Kilian Q Weinberger · 2018
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Two at once: Enhancing learning and generalization capacities via ibn-net
Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang · 2018
Cited alongside, same era.
Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John C. Duchi · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, and Silvio Savarese · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Cited alongside, same era.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Progressive domain adaptation for object detection
Han-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai, Wei-Chih Hung, Hung-Yu Tseng, Maneesh Kumar Singh, and Ming-Hsuan Yang · 2020
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Flag: Adversarial data augmentation for graph neural networks
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein · 2020
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Meta dropout: Learning to perturb latent features for generalization
Haebeom Lee, Taewook Nam, Eunho Yang, and Sung Ju Hwang · 2020
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Adversarial style mining for one-shot unsupervised domain adaptation
Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, and Yi Yang · 2020
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas G. Dietterich · 2019
Cited alongside, same era.
Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
Cited alongside, same era.
Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
Cited alongside, same era.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
Cited alongside, same era.
Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
Cited alongside, same era.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Cited alongside, same era.
Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli · 2020
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A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, and Moritz Hardt · 2020
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Cross-domain few-shot classification via learned feature-wise transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, and Ming-Hsuan Yang · 2020
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Recognizing instagram filtered images with feature de-stylization
Zhe Wu, Zuxuan Wu, Bharat Singh, and Larry S. Davis · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le · 2020
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Adversarial autoaugment
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2020
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Maximum-entropy adversarial data augmentation for improved generalization and robustness
Long Zhao, Ting Liu, Xi Peng, and Dimitris N. Metaxas · 2020
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu · 2020
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On feature normalization and data augmentation
Boyi Li, Felix Wu, Ser-Nam Lim, Serge J. Belongie, and Kilian Q. Weinberger · 2021
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, and Trevor Darrell · 2021
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