Fetching the paper…
Reading the bibliography…
Deep neural networks are known to be vulnerable to adversarially perturbed inputs.
Incorporating second-order functional knowledge for better option pricing
Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, and René Garcia · 2001
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Does network width really help adversarial robustness?
Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, and Quanquan Gu · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 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.
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.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Suppressing the unusual: towards robust cnns using symmetric activation functions
Qiyang Zhao and Lewis D. Griffin · 2016
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 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.
Adnan Siraj Rakin, Jinfeng Yi, Boqing Gong, and Deliang Fan · 2018
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Later among the works it cites.
Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2020
Later among the works it cites.
Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2020
Later among the works it cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Later among the works it cites.
Exactly computing the local lipschitz constant of relu networks
Matt Jordan and Alexandros G Dimakis · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V. Le · 2018
Cited alongside, same era.
Adversarial defense via data dependent activation function and total variation minimization
Bao Wang, Alex T Lin, Zuoqiang Shi, Wei Zhu, Penghang Yin, Andrea L Bertozzi, and Stanley J Osher · 2018
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
Cited alongside, same era.
An evaluation of parametric activation functions for deep learning
Luke B. Godfrey · 2019
Cited alongside, same era.
Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2019
Cited alongside, same era.
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
Cited alongside, same era.
On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
Cited alongside, same era.
Later among the works it cites.
Bag of tricks for adversarial training
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, and Jun Zhu · 2020
Later among the works it cites.
Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and Zico Kolter · 2020
Later among the works it cites.
Splash: Learnable activation functions for improving accuracy and adversarial robustness
Mohammadamin Tavakoli, Forest Agostinelli, and Pierre Baldi · 2020
Later among the works it cites.
Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, and Quoc V Le · 2020
Later among the works it cites.
A closer look at accuracy vs. robustness
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Russ R Salakhutdinov, and Kamalika Chaudhuri · 2020
Later among the works it cites.
A universal law of robustness via isoperimetry
Sébastien Bubeck and Mark Sellke · 2021
Closest in time.
Training robust neural networks using lipschitz bounds
Patricia Pauli, Anne Koch, Julian Berberich, Paul Kohler, and Frank Allgower · 2021
Closest in time.
Fixing data augmentation to improve adversarial robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian, Florian Stimberg, Olivia Wiles, and Timothy A. Mann · 2021
Closest in time.
Improving adversarial robustness using proxy distributions
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, and Prateek Mittal · 2021
Closest in time.
Low curvature activations reduce overfitting in adversarial training
Vasu Singla, Sahil Singla, David Jacobs, and Soheil Feizi · 2021
Closest in time.