Fetching the paper…
Reading the bibliography…
Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations, especially on the classification task.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, and Mehdi Mirza · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
Earlier work this paper cites.
Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Semantic segmentation using adversarial networks
Pauline Luc, Camille Couprie, and Soumith Chintala · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Earlier work this paper cites.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Earlier work this paper cites.
Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade
Xiaoxiao Li, Ziwei Liu, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2017
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
Cited alongside, same era.
Universal adversarial perturbations against semantic image segmentation
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, and Volker Fischer · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2017
Cited alongside, same era.
Adversarial examples for semantic segmentation and object detection
Deflecting adversarial attacks with pixel deflection
Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, and James Storer · 2018
Later among the works it cites.
Improving the generalization of adversarial training with domain adaptation
Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft · 2018
Later among the works it cites.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2018
Later among the works it cites.
Characterizing adversarial examples based on spatial consistency information for semantic segmentation
Chaowei Xiao, Ruizhi Deng, Bo Li, Fisher Yu, Mingyan Liu, and Dawn Song · 2018
Later among the works it cites.
On the robustness of redundant teacher-student frameworks for semantic segmentation
Andreas Bar, Fabian Huger, Peter Schlicht, and Tim Fingscheidt · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille · 2017
Cited alongside, same era.
Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
Cited alongside, same era.
On the robustness of semantic segmentation models to adversarial attacks
Anurag Arnab, Ondrej Miksik, and Philip HS Torr · 2018
Cited alongside, same era.
On the robustness of the cvpr 2018 white-box adversarial example defenses
Anish Athalye and Nicholas Carlini · 2018
Cited alongside, same era.
Curriculum adversarial training
Qi-Zhi Cai, Min Du, Chang Liu, and Dawn Song · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 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.
Defending against adversarial attacks using random forest
Yifan Ding, Liqiang Wang, Huan Zhang, Jinfeng Yi, Deliang Fan, and Boqing Gong · 2019
Later among the works it cites.
Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen · 2019
Later among the works it cites.
Comdefend: An efficient image compression model to defend adversarial examples
Xiaojun Jia, Xingxing Wei, Xiaochun Cao, and Hassan Foroosh · 2019
Later among the works it cites.
Barrage of random transforms for adversarially robust defense
Edward Raff, Jared Sylvester, Steven Forsyth, and Mark McLean · 2019
Later among the works it cites.
Bilateral adversarial training: Towards fast training of more robust models against adversarial attacks
Jianyu Wang and Haichao Zhang · 2019
Later among the works it cites.
Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
Later among the works it cites.
Defense against adversarial attacks using feature scattering-based adversarial training
Haichao Zhang and Jianyu Wang · 2019
Later among the works it cites.
Robust semantic segmentation by redundant networks with a layer-specific loss contribution and majority vote
Andreas Bar, Marvin Klingner, Serin Varghese, Fabian Huger, Peter Schlicht, and Tim Fingscheidt · 2020
Closest in time.
Improved noise and attack robustness for semantic segmentation by using multi-task training with self-supervised depth estimation
Marvin Klingner, Andreas Bar, and Tim Fingscheidt · 2020
Closest in time.
Multitask learning strengthens adversarial robustness
Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, and Carl Vondrick · 2020
Closest in time.
Boosting the transferability of adversarial samples via attention
Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Michael R Lyu, and Yu-Wing Tai · 2020
Closest in time.