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Certified robustness guarantee gauges a model's robustness to test-time attacks and can assess the model's readiness for deployment in the real world.
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Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise
Olivier R Joubert, Guillaume A Rousselet, Michèle Fabre-Thorpe, and Denis Fize · 2009
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Kullback-Leibler Divergence
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Monte carlo methods
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Intriguing properties of neural networks
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Understanding how image quality affects deep neural networks
Samuel Dodge and Lina Karam · 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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An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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ZOO: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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EAD: elastic-net attacks to deep neural networks via adversarial examples
Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2018
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On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2018
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Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 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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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 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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Towards verifying robustness of neural networks against a family of semantic perturbations
Jeet Mohapatra, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel · 2020
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Certify or predict: Boosting certified robustness with compositional architectures
Mark Niklas Mueller, Mislav Balunovic, and Martin Vechev · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 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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Randomized smoothing of all shapes and sizes
Greg Yang, Tony Duan, J Edward Hu, Hadi Salman, Ilya Razenshteyn, and Jerry Li · 2020
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Generating adversarial examples with adversarial networks
Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song · 2018
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Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
Cited alongside, same era.
Phase consistent ecological domain adaptation
Yanchao Yang, Dong Lao, Ganesh Sundaramoorthi, and Stefano Soatto · 2020
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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 · 2020
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http://www.scholarpedia.org/article/1/f_noise , 2021
1/f noise · 2021
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A winning hand: Compressing deep networks can improve out-of-distribution robustness
James Diffenderfer, Brian R Bartoldson, Shreya Chaganti, Jize Zhang, and Bhavya Kailkhura · 2021
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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, et al · 2021
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Unsolved problems in ml safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2021
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On the effectiveness of adversarial training against common corruptions
Klim Kireev, Maksym Andriushchenko, and Nicolas Flammarion · 2021
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Tss: Transformation-specific smoothing for robustness certification
Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, and Bo Li · 2021
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How robust are randomized smoothing based defenses to data poisoning?
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm · 2021
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On interaction between augmentations and corruptions in natural corruption robustness
Eric Mintun, Alexander Kirillov, and Saining Xie · 2021
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How robust are model rankings: A leaderboard customization approach for equitable evaluation
Swaroop Mishra and Anjana Arunkumar · 2021
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Fixing data augmentation to improve adversarial robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A Calian, Florian Stimberg, Olivia Wiles, and Timothy Mann · 2021
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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A fourier-based framework for domain generalization
Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian · 2021
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