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Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog.
Improving robustness without sacrificing accuracy with patch gaussian augmentation
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin D. Cubuk · 1906
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SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, and Bo Li · 1906
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A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, and Justin Gilmer · 1906
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 1907
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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Sven Gowal, Chongli Qin, Po-Sen Huang, Taylan Cemgil, Krishnamurthy Dvijotham, Timothy Mann, and Pushmeet Kohli · 1912
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Some methods of speeding up the convergence of iteration methods
Boris T Polyak · 1964
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A method of solving a convex programming problem with convergence rate o ( 1 / k 2 ) o(1/k^{2})
Yurii Nesterov · 1983
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Statistical learning theory
Vladimir Vapnik · 1998
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A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2001
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2006
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Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller · 2007
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2010
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Unbiased look at dataset bias
Antonio Torralba, Alexei A Efros, and others · 2011
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, and others · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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 J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and koray kavukcuoglu · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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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, Xin Zhang, Jake Zhao, and Karol Zieba · 2016
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Deep neural networks for YouTube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Examining the impact of blur on recognition by convolutional networks
Igor Vasiljevic, Ayan Chakrabarti, and Gregory Shakhnarovich · 2016
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2019
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Mixup as locally linear out-of-manifold regularization
Hongyu Guo, Yongyi Mao, and Richong Zhang · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
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Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2019
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Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Adversarial transformation networks: Learning to generate adversarial examples
Shumeet Baluja and Ian Fischer · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Gender-from-iris or gender-from-mascara?
Andrey Kuehlkamp, Benedict Becker, and Kevin Bowyer · 2017
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Enhanced deep residual networks for single image super-resolution, 2017
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
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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 can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang · 2019
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Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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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
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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
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Robustbench: a standardized adversarial robustness benchmark, 2020
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2020
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New Quality Measures for Adversarial Attacks with Applications to Secure Communication
Muhammad Zaid Hameed · 2020
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Compounding the performance improvements of assembled techniques in a convolutional neural network, 2020
Jungkyu Lee, Taeryun Won, Tae Kwan Lee, Hyemin Lee, Geonmo Gu, and Kiho Hong · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and J. Zico Kolter · 2020
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Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-tao Xia, and Yisen Wang · 2020
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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Perceptually constrained adversarial attacks, 2021
Muhammad Zaid Hameed and András György · 2021
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On the effectiveness of adversarial training against common corruptions, 2021
Klim Kireev, Maksym Andriushchenko, and Nicolas Flammarion · 2021
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Perceptual adversarial robustness: Defense against unseen threat models
Cassidy Laidlaw, Sahil Singla, and Soheil Feizi · 2021
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The surprising effectiveness of linear unsupervised image-to-image translation
Eitan Richardson and Yair Weiss · 2021
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