Fetching the paper…
Reading the bibliography…
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness).
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Document image defect models
Henry S Baird · 1992
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Andrew R Barron · 1993
Earlier work this paper cites.
Effective training of a neural network character classifier for word recognition
Larry S. Yaeger, Richard F. Lyon, and Brandyn J. Webb · 1997
Earlier work this paper cites.
An empirical evaluation of deep architectures on problems with many factors of variation
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra, and Yoshua Bengio · 2007
Earlier work this paper cites.
Robust optimization
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 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 · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, et al · 2015
Earlier work this paper cites.
Manitest: Are classifiers really invariant?
A. Fawzi and P. Frossard · 2015
Earlier work this paper cites.
Spatial Transformer Networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2015
Earlier work this paper cites.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 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.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
TI-POOLING: Transformation-invariant pooling for feature learning in convolutional neural networks
Dmitry Laptev, Nikolay Savinov, Joachim M Buhmann, and Marc Pollefeys · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Improving the robustness of deep neural networks via stability training
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Universal function approximation by deep neural nets with bounded width and relu activations
Boris Hanin · 2017
Cited alongside, same era.
Geometric robustness of deep networks: analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2018
Later among the works it cites.
Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Later among the works it cites.
Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
Later among the works it cites.
Cascade adversarial machine learning regularized with a unified embedding
Taesik Na, Jong Hwan Ko, and Saibal Mukhopadhyay · 2018
Later among the works it cites.
Certified defenses against adversarial examples
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Christina Heinze-Deml and Nicolai Meinshausen · 2017
Cited alongside, same era.
Rotation equivariant vector field networks
Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia · 2017
Cited alongside, same era.
Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto · 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.
Towards practical verification of machine learning: The case of computer vision systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
Cited alongside, same era.
Harmonic networks: Deep translation and rotation equivariance
Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow · 2017
Cited alongside, same era.
Oriented response networks
Yanzhao Zhou, Qixiang Ye, Qiang Qiu, and Jianbin Jiao · 2017
Cited alongside, same era.
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Later among the works it cites.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
Later among the works it cites.
Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
Later among the works it cites.
Learning steerable filters for rotation equivariant CNNs
Maurice Weiler, Fred A Hamprecht, and Martin Storath · 2018
Later among the works it cites.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Later among the works it cites.
Learning rotation-invariant and Fisher discriminative convolutional neural networks for object detection
Gong Cheng, Junwei Han, Peicheng Zhou, and Dong Xu · 2019
Closest in time.
Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
Closest in time.
Adversarial training can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, and Percy Liang · 2019
Closest in time.
Equivariant Transformer Networks
Kai Sheng Tai, Peter Bailis, and Gregory Valiant · 2019
Closest in time.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
Closest in time.
Unsupervised data augmentation
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le · 2019
Closest in time.
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
Closest in time.