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Neural networks lack adversarial robustness, i.e., they are vulnerable to adversarial examples that through small perturbations to inputs cause incorrect predictions.
Deflecting adversarial attacks
Qin, Y., Frosst, N., Raffel, C., Cottrell, G., and Hinton, G · 2002
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Practical variational inference for neural networks
Graves, A · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. J., and Fergus, R · 2014
Earlier work this paper cites.
Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Earlier work this paper cites.
Variational dropout and the local reparameterization trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
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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. S., Berg, A. C., and Li, F.-F · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Launch and iterate: Reducing prediction churn
Milani Fard, M., Cormier, Q., Canini, K., and Gupta, M · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2017
Cited alongside, same era.
Mixup: Beyond empirical risk minimization
Zhang, H., Cissé, M., Dauphin, Y., and Lopez-Paz, D · 2018
Later among the works it cites.
Distribution density, tails, and outliers in machine learning: Metrics and applications
Carlini, N., Erlingsson, Ú., and Papernot, N · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. G · 2019
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Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
Kull, M., Perello Nieto, M., Kängsepp, M., Silva Filho, T., Song, H., and Flach, P · 2019
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When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G. E · 2019
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Folding: Why good models sometimes make spurious recommendations
Xin, D., Mayoraz, N., Pham, H., Lakshmanan, K., and Anderson, J. R · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Cited alongside, same era.
Evaluation methodology for attacks against confidence thresholding models
Goodfellow, I., Qin, Y., and Berthelot, D · 2018
Cited alongside, same era.
Decision boundary analysis of adversarial examples
He, W., Li, B., and Song, D · 2018
Cited alongside, same era.
Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases
Stock, P. and Cissé, M · 2018
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D
Cited in the paper.
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J., Ren, J., and Nado, Z · 2019
Later among the works it cites.
On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Thulasidasan, S., Chennupati, G., Bilmes, J. A., Bhattacharya, T., and Michalak, S · 2019
Later among the works it cites.
Me-net: Towards effective adversarial robustness with matrix estimation
Yang, Y., Zhang, G., Katabi, D., and Xu, Z · 2019
Later among the works it cites.
Confidence-calibrated adversarial training: Generalizing to unseen attacks
Stutz, D., Hein, M., and Schiele, B · 2020
Closest in time.
Batchensemble: An alternative approach to efficient ensemble and lifelong learning
Wen, Y., Tran, D., and Ba, J · 2020
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Zhang, J., Kailkhura, B., and Han, T. Y.-J · 2020
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