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Skip connections are an essential component of current state-of-the-art deep neural networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, 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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael J Wilber, and Serge Belongie · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Practical black-box attacks on deep neural networks using efficient query mechanisms
Arjun Nitin Bhagoji, Warren He, Bo Li, and Dawn Song · 2018
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Ead: elastic-net attacks to deep neural networks via adversarial examples
Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2018
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Boosting adversarial attacks with momentum
Hilbert-based generative defense for adversarial examples
Yang Bai, Yan Feng, Yisen Wang, Tao Dai, Shu-Tao Xia, and Yong Jiang · 2019
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AdverTorch v0.1: An adversarial robustness toolbox based on pytorch
Gavin Weiguang Ding, Luyu Wang, and Xiaomeng Jin · 2019
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Evading defenses to transferable adversarial examples by translation-invariant attacks
Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Enhancing adversarial example transferability with an intermediate level attack
Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, and Ser-Nam Lim · 2019
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Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen · 2019
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Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Cited alongside, same era.
Robust physical-world attacks on deep learning models
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
Cited alongside, same era.
Black-box adversarial attacks on video recognition models
Linxi Jiang, Xingjun Ma, Shaoxiang Chen, James Bailey, and Yu-Gang Jiang · 2019
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Understanding adversarial attacks on deep learning based medical image analysis systems
Xingjun Ma, Yuhao Niu, Lin Gu, Yisen Wang, Yitian Zhao, James Bailey, and Feng Lu · 2019
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Sparsefool: a few pixels make a big difference
Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2019
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First-order adversarial vulnerability of neural networks and input dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, and David Lopez-Paz · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
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