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The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A theory of causal learning in children: causal maps and bayes nets
Alison Gopnik, Clark Glymour, David M Sobel, Laura E Schulz, Tamar Kushnir, and David Danks · 2004
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Interventions and causal inference
Frederick Eberhardt and Richard Scheines · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Causality
Judea Pearl · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. 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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Universal adversarial perturbations against semantic image segmentation
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, and Volker Fischer · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 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.
Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 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.
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.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Invariance, causality and robustness
Peter Bühlmann · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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A calculus for stochastic interventions: Causal effect identification and surrogate experiments
Juan Correa and Elias Bareinboim · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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Sigua: Forgetting may make learning with noisy labels more robust
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
Cited alongside, same era.
Generalization in anti-causal learning
Niki Kilbertus, Giambattista Parascandolo, and Bernhard Schölkopf · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey · 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.
Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Simulation-based adversarial test generation for autonomous vehicles with machine learning components
Cumhur Erkan Tuncali, Georgios Fainekos, Hisahiro Ito, and James Kapinski · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Cited alongside, same era.
Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, and Masashi Sugiyama · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Representation learning via invariant causal mechanisms
Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Holger Buesing, and Charles Blundell · 2020
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Boosting adversarial training with hypersphere embedding
Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Jun Zhu, and Hang Su · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and Zico Kolter · 2020
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2020
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Counterfactual generative networks
Axel Sauer and Andreas Geiger · 2020
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Second-order provable defenses against adversarial attacks
Sahil Singla and Soheil Feizi · 2020
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Long-tailed classification by keeping the good and removing the bad momentum causal effect
Kaihua Tang, Jianqiang Huang, and Hanwang Zhang · 2020
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Few-shot domain adaptation by causal mechanism transfer
Takeshi Teshima, Issei Sato, and Masashi Sugiyama · 2020
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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Part-dependent label noise: Towards instance-dependent label noise
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, and Masashi Sugiyama · 2020
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Maximum mean discrepancy test is aware of adversarial attacks
Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, and Masashi Sugiyama · 2021
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Conditional variance penalties and domain shift robustness
Christina Heinze-Deml and Nicolai Meinshausen · 2021
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Adversarial visual robustness by causal intervention
Kaihua Tang, Mingyuan Tao, and Hanwang Zhang · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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