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Efforts to improve the adversarial robustness of convolutional neural networks have primarily focused on developing more effective adversarial training methods.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian 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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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 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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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 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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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Adversarial robustness through local linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, and Pushmeet Kohli · 2019
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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
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Mixconv: Mixed depthwise convolutional kernels
Mingxing Tan and Quoc V Le · 2019
Cited alongside, same era.
Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Cited alongside, same era.
Anti-bandit neural architecture search for model defense
Hanlin Chen, Baochang Zhang, Song Xue, Xuan Gong, Hong Liu, Rongrong Ji, and David Doermann · 2020
Cited alongside, same era.
Robust overfitting may be mitigated by properly learned smoothening
Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, and Zhangyang Wang · 2021
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Res2net: A new multi-scale backbone architecture
Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, and Philip Torr · 2021
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Improving robustness using generated data
Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles, Florian Stimberg, Dan Andrei Calian, and Timothy Mann · 2021
Later among the works it cites.
Exploring architectural ingredients of adversarially robust deep neural networks
Hanxun Huang, Yisen Wang, Sarah Erfani, Quanquan Gu, James Bailey, and Xingjun Ma · 2021
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Fapn: Feature-aligned pyramid network for dense image prediction
Shihua Huang, Zhichao Lu, Ran Cheng, and Cheng He · 2021
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Multi-objective search of robust neural architectures against multiple types of adversarial attacks
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Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2020
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Cited alongside, same era.
Adversarially robust neural architectures
Minjing Dong, Yanxi Li, Yunhe Wang, and Chang Xu · 2020
Cited alongside, same era.
Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2020
Cited alongside, same era.
When nas meets robustness: In search of robust architectures against adversarial attacks
Minghao Guo, Yuzhe Yang, Rui Xu, Ziwei Liu, and Dahua Lin · 2020
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Enhua Wu · 2020
Cited alongside, same era.
Discovering robust convolutional architecture at targeted capacity: A multi-shot approach
Xuefei Ning, Junbo Zhao, Wenshuo Li, Tianchen Zhao, Yin Zheng, Huazhong Yang, and Yu Wang · 2020
Cited alongside, same era.
Jia Liu and Yaochu Jin · 2021
Later among the works it cites.
Advrush: Searching for adversarially robust neural architectures
Jisoo Mok, Byunggook Na, Hyeokjun Choe, and Sungroh Yoon · 2021
Later among the works it cites.
Bag of tricks for adversarial training
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, and Jun Zhu · 2021
Later among the works it cites.
Reducing excessive margin to achieve a better accuracy vs. robustness trade-off
Rahul Rade and Seyed-Mohsen Moosavi-Dezfooli · 2021
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Data augmentation can improve robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, and Timothy Mann · 2021
Later among the works it cites.
Low curvature activations reduce overfitting in adversarial training
Vasu Singla, Sahil Singla, Soheil Feizi, and David Jacobs · 2021
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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 · 2021
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Resnet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, and Hervé Jégou · 2021
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Parameterizing activation functions for adversarial robustness
Sihui Dai, Saeed Mahloujifar, and Prateek Mittal · 2022
Closest in time.
Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Robust learning meets generative models: Can proxy distributions improve adversarial robustness?
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, and Prateek Mittal · 2022
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Self-ensemble adversarial training for improved robustness
Hongjun Wang and Yisen Wang · 2022
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Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
Zhenyu Zhu, Fanghui Liu, Grigorios G Chrysos, and Volkan Cevher · 2022
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