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The literature on robustness towards common corruptions shows no consensus on whether adversarial training can improve the performance in this setting.
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D · 1907
Earlier work this paper cites.
Improving generalization performance using double backpropagation
Drucker, H. and LeCun, Y · 1992
Earlier work this paper cites.
Training with noise is equivalent to Tikhonov regularization
Bishop, C. M · 1995
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
Earlier work this paper cites.
A study and comparison of human and deep learning recognition performance under visual distortions
Dodge, S. and Karam, L · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Generalisation in humans and deep neural networks
Geirhos, R., Temme, C. R. M., Rauber, J., Schütt, H. H., Bethge, M., and Wichmann, F. A · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Earlier work this paper cites.
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Shaham, U., Yamada, Y., and Negahban, S · 2018
Earlier work this paper cites.
On the suitability of lp-norms for creating and preventing adversarial examples
Sharif, M., Bauer, L., and Reiter, M. K · 2018
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Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2018
Earlier work this paper cites.
Generalizing to unseen domains via adversarial data augmentation
Volpi, R., Namkoong, H., Sener, O., Duchi, J. C., Murino, V., and Savarese, S · 2018
Earlier work this paper cites.
Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A. and Weiss, Y · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
Cited alongside, same era.
Robustness (python library), 2019
Engstrom, L., Ilyas, A., Salman, H., Santurkar, S., and Tsipras, D · 2019
Cited alongside, same era.
Adversarial examples are a natural consequence of test error in noise
Ford, N., Gilmer, J., Carlini, N., and Cubuk, D · 2019
Cited alongside, same era.
Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Understanding and improving fast adversarial training
Andriushchenko, M. and Flammarion, N · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F. and Hein, M · 2020
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Robustbench: a standardized adversarial robustness benchmark
Croce, F., Andriushchenko, M., Sehwag, V., Flammarion, N., Chiang, M., Mittal, P., and Hein, M · 2020
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Bridging the performance gap between fgsm and pgd adversarial training
Huang, T., Menkovski, V., Pei, Y., and Pechenizkiy, M · 2020
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Hold me tight! influence of discriminative features on deep network boundaries
Ortiz-Jimenez, G., Modas, A., Moosavi-Dezfooli, S.-M., and Frossard, P · 2020
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Hendrycks, D. and Dietterich, T · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
Cited alongside, same era.
Transfer of adversarial robustness between perturbation types
Kang, D., Sun, Y., Brown, T., Hendrycks, D., and Steinhardt, J · 2019
Cited alongside, same era.
Robustness via curvature regularization, and vice versa
Moosavi-Dezfooli, S.-M., Fawzi, A., Uesato, J., and Frossard, P · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 2019
Cited alongside, same era.
Adversarial training for free!
Shafahi, A., Najibi, M., Ghiasi, A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T · 2019
Cited alongside, same era.
First-order adversarial vulnerability of neural networks and input dimension
Simon-Gabriel, C.-J., Ollivier, Y., Bottou, L., Schölkopf, B., and Lopez-Paz, D · 2019
Cited alongside, same era.
A simple way to make neural networks robust against diverse image corruptions
Rusak, E., Schott, L., Zimmermann, R. S., Bitterwolf, J., Bringmann, O., Bethge, M., and Brendel, W · 2020
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Improving robustness against common corruptions by covariate shift adaptation
Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., and Bethge, M · 2020
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Universal adversarial training
Shafahi, A., Najibi, M., Xu, Z., Dickerson, J., Davis, L. S., and Goldstein, T · 2020
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Measuring robustness to natural distribution shifts in image classification
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., and Schmidt, L · 2020
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Improved sample complexities for deep neural networks and robust classification via an all-layer margin
Wei, C. and Ma, T · 2020
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Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
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Adversarial examples improve image recognition
Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A. L., and Le, Q. V · 2020
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al · 2021
Closest in time.
Perceptual adversarial robustness: Generalizable defenses against unforeseen threat models
Laidlaw, C., Singla, S., and Feizi, S · 2021
Closest in time.
On interaction between augmentations and corruptions in natural corruption robustness
Mintun, E., Kirillov, A., and Xie, S · 2021
Closest in time.
Adversarially trained models with test-time covariate shift adaptation
Nandy, J., Saha, S., Hsu, W., Lee, M. L., and Zhu, X. X · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Closest in time.