Fetching the paper…
Reading the bibliography…
Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy.
Nasir Ahmed, T Natarajan, and Kamisetty R Rao · 1974
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
When face recognition meets with deep learning: An evaluation of convolutional neural networks for face recognition
Guosheng Hu, Yongxin Yang, Dong Yi, Josef Kittler, William Christmas, Stan Z Li, and Timothy Hospedales · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Segmentation and image analysis of abnormal lungs at CT: Current approaches, challenges, and future trends
Awais Mansoor, Ulas Bagci, Brent Foster, Ziyue Xu, Georgios Z Papadakis, Les R Folio, Jayaram K Udupa, and Daniel J Mollura · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
BranchyNet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and H. T. Kung · 2017
Earlier work this paper cites.
Spatially adaptive computation time for residual networks
Michael Figurnov, Maxwell D Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, and Ruslan Salakhutdinov · 2017
Earlier work this paper cites.
Modulating early visual processing by language
Harm De Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C Courville · 2017
Cited alongside, same era.
Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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.
Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
Cited alongside, same era.
Multi-scale dense networks for resource efficient image classification
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
Later among the works it cites.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Later among the works it cites.
Adversarial robustness vs model compression, or both?
Shaokai Ye, Kaidi Xu, Sijia Liu, Hao Cheng, Jan-Henrik Lambrechts, Huan Zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, and Xue Lin · 2019
Later among the works it cites.
Model compression with adversarial robustness: A unified optimization framework
Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, and Ji Liu · 2019
Later among the works it cites.
Slimmable neural networks
Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas Huang · 2019
Later among the works it cites.
Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Weinberger · 2018
Cited alongside, same era.
SkipNet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, and Joseph E Gonzalez · 2018
Cited alongside, same era.
FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Cited alongside, same era.
Adjustable real-time style transfer
Mohammad Babaeizadeh and Golnaz Ghiasi · 2018
Cited alongside, same era.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 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.
Jérôme Rony, Luiz G Hafemann, Luiz S Oliveira, Ismail Ben Ayed, Robert Sabourin, and Eric Granger · 2019
Later among the works it cites.
Shallow-deep networks: Understanding and mitigating network overthinking
Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitras · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Later among the works it cites.
Controllable artistic text style transfer via shape-matching GAN
Shuai Yang, Zhangyang Wang, Zhaowen Wang, Ning Xu, Jiaying Liu, and Zongming Guo · 2019
Later among the works it cites.
Deep network interpolation for continuous imagery effect transition
Xintao Wang, Ke Yu, Chao Dong, Xiaoou Tang, and Chen Change Loy · 2019
Later among the works it cites.
On the connection between adversarial robustness and saliency map interpretability
Christian Etmann, Sebastian Lunz, Peter Maass, and Carola-Bibiane Schönlieb · 2019
Later among the works it cites.
Adversarial robustness: From self-supervised pre-training to fine-tuning
Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, and Zhangyang Wang · 2020
Closest in time.
Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Yuille, and Quoc V Le · 2020
Closest in time.
Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference
Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang · 2020
Closest in time.
MMA training: Direct input space margin maximization through adversarial training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang · 2020
Closest in time.
Intriguing properties of adversarial training at scale
Cihang Xie and Alan Yuille · 2020
Closest in time.
Jacobian adversarially regularized networks for robustness
Alvin Chan, Yi Tay, Yew Soon Ong, and Jie Fu · 2020
Closest in time.