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The reliability of neural networks is essential for their use in safety-critical applications.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H · 2000
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Dataset shift in machine learning
Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Wavenet: A generative model for raw audio
van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. W., and Kavukcuoglu, K · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
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Revisiting batch normalization for practical domain adaptation
Li, Y., Wang, N., Shi, J., Liu, J., and Hou, X · 2017
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Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
Samangouei, P., Kabkab, M., and Chellappa, R · 2018
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Exploring the landscape of spatial robustness
Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., and Madry, A · 2019
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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
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Benchmarking neural network robustness to common corruptions and perturbations
Understanding and mitigating the tradeoff between robustness and accuracy
Raghunathan, A., Xie, S. M., Yang, F., Duchi, J. C., and Liang, P · 2020
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Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, J. Z · 2020
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Adversarial training is a form of data-dependent operator norm regularization
Roth, K., Kilcher, Y., and Hofmann, T · 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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A survey of unsupervised deep domain adaptation
Wilson, G. and Cook, D. J · 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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Hendrycks, D. and Dietterich, T. G · 2019
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A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T. M · 2019
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A fourier perspective on model robustness in computer vision
Yin, D., Lopes, R. G., Shlens, J., Cubuk, E. D., and Gilmer, J · 2019
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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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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Gowal, S., Qin, C., Uesato, J., Mann, T. A., and Kohli, P · 2020
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Augmix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2020
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CLIP: cheap lipschitz training of neural networks
Bungert, L., Raab, R., Roith, T., Schwinn, L., and Tenbrinck, D · 2021
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Fast minimum-norm adversarial attacks through adaptive norm constraints
Pintor, M., Roli, F., Brendel, W., and Biggio, B · 2021
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Fixing data augmentation to improve adversarial robustness
Rebuffi, S., Gowal, S., Calian, D. A., Stimberg, F., Wiles, O., and Mann, T. A · 2021
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Adversarial purification with score-based generative models
Yoon, J., Hwang, S. J., and Lee, J · 2021
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MEMO: test time robustness via adaptation and augmentation
Zhang, M., Levine, S., and Finn, C · 2021
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