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In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Léon Bottou · 2010
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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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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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Mixup: Beyond empirical risk minimization
Yann N Dauphin Hongyi Zhang, Moustapha Cisse and David Lopez-Paz · 2018
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Second-order adversarial attack and certifiable robustness
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Towards fast computation of certified robustness for ReLU networks
Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, and Inderjit Dhillon · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J. Zico Kolter · 2018
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Are labels required for improving adversarial robustness?
Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, and Pushmeet Kohli · 2019
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Noise2self: Blind denoising by self-supervision networks with l-inf-dist neurons
Loic Royer Joshua Batson · 2019
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Robustness to adversarial perturbations in learning from incomplete data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama, and Takeru Miyato · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, and Greg Yang · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Electra: Pre-training text encoders as discriminators rather than generators
Quoc V Le Kevin Clark, Minh-Thang Luong and Christopher D Manning · 2020
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Curse of dimensionality on randomized smoothing for certifiable robustness
Aounon Kumar, Alexander Levine, Tom Goldstein, and Soheil Feizi · 2020
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Denoised smoothing: A provable defense for pretrained classifiers
Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, and J Zico Kolter · 2020
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Randomized smoothing of all shapes and sizes
Greg Yang, Tony Duan, Edward Hu, Hadi Salman, Ilya Razenshteyn, and Jerry Li · 2020
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Noise2same: Optimizing a self-supervised bound for image denoising
Shuiwang Ji Yaochen Xie, Zhengyang Wang · 2020
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Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Adversarially robust generalization just requires more unlabeled data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, and Liwei Wang · 2019
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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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Adversarial training and provable defenses: Bridging the gap
Mislav Balunovic and Martin Vechev · 2020
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Random smoothing might be unable to certify ℓ ∞ \ell_{\infty} robustness for high-dimensional images
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A simple framework for contrastive learning of visual representations
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Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexander Kolesnikov, Alexey Dosovitskiy, Dirk Weissenborn, Georg Heigold, Jakob Uszkoreit, Lucas Beyer, Matthias Minderer, Mostafa Dehghani, Neil Houlsby, Sylvain Gelly, Thomas Unterthiner, and Xiaohua Zhai · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Macer: Attack-free and scalable robust training via maximizing certified radius
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang · 2021
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Towards certifying l-infinity robustness using neural networks with l-inf-dist neurons
Bohang Zhang, Tianle Cai, Zhou Lu, Di He, and Liwei Wang · 2021
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(certified!!) adversarial robustness for free!
Nicholas Carlini, Florian Tramer, J Zico Kolter, et al · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Boosting randomized smoothing with variance reduced classifiers
Miklós Z Horváth, Mark Niklas Müller, Marc Fischer, and Martin Vechev · 2022
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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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