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Achieving robustness to distributional shift is a longstanding and challenging goal of computer vision.
The Fourier transform and its applications
R. N. Bracewell and R. N. Bracewell · 1986
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
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A study and comparison of human and deep learning recognition performance under visual distortions
S. Dodge and L. Karam · 2017
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Measuring the tendency of CNNs to learn surface statistical regularities
J. Jo and Y. Bengio · 2017
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Feature visualization
C. Olah, A. Mordvintsev, and L. Schubert · 2017
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The effects of JPEG and JPEG2000 compression on attacks using adversarial examples
A. E. Aydemir, A. Temizel, and T. T. Temizel · 2018
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Why do deep convolutional networks generalize so poorly to small image transformations?
A. Azulay and Y. Weiss · 2018
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Shield: Fast, practical defense and vaccination for deep learning using jpeg compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, and D. H. Chau · 2018
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Generalisation in humans and deep neural networks
R. Geirhos, C. R. Temme, J. Rauber, H. H. Schütt, M. Bethge, and F. A. Wichmann · 2018
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Motivating the rules of the game for adversarial example research
J. Gilmer, R. P. Adams, I. Goodfellow, D. Andersen, and G. E. Dahl · 2018
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Excessive invariance causes adversarial vulnerability
J.-H. Jacobsen, J. Behrmann, R. Zemel, and M. Bethge · 2018
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
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Simple black-box adversarial attacks
C. Guo, J. R. Gardner, Y. You, A. G. Wilson, and K. Q. Weinberger · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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Adversarial examples are not bugs, they are features
A. Ilyas, S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry · 2019
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Feature distillation: DNN-oriented JPEG compression against adversarial examples
Z. Liu, Q. Liu, T. Liu, Y. Wang, and W. Wen · 2019
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Towards deep learning models resistant to adversarial attacks
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Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
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Band-limited training and inference for convolutional neural networks
A. Dziedzic, J. Paparrizos, S. Krishnan, A. Elmore, and M. Franklin · 2019
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Adversarial examples are a natural consequence of test error in noise
N. Ford, J. Gilmer, N. Carlini, and E. D. Cubuk · 2019
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Do ImageNet classifiers generalize to ImageNet?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2019
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On the structural sensitivity of deep convolutional networks to the directions of fourier basis functions
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AugMix: A simple method to improve robustness and uncertainty under data shift
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