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Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data.
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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M. Lapin, M. Hein, and B. Schiele · 2016
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S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 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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N. Carlini and D. Wagner · 2017
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G. Cohen, S. Afshar, J. Tapson, and A. van Schaik · 2017
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C. Guo, G. Pleiss, Y. Sun, and K. Weinberger · 2017
A randomized gradient-free attack on relu networks
F. Croce and M. Hein · 2018
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T. DeVries and G. W. Taylor · 2018
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K. Lee, H. Lee, K. Lee, and J. Shin · 2018
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S. Liang, Y. Li, and R. Srikant · 2018
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Differentiable abstract interpretation for provably robust neural networks
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Formal guarantees on the robustness of a classifier against adversarial manipulation
M. Hein and M. Andriushchenko · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
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Leveraging uncertainty information from deep neural networks for disease detection
C. Leibig, V. Allken, M. S. Ayhan, P. Berens, and S. Wahl · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Understanding deep neural networks with rectified linear unit
R. Arora, A. Basuy, P. Mianjyz, and A. Mukherjee · 2018
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M. Mirman, T. Gehr, and M. Vechev · 2018
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Do deep generative models know what they don’t know?
E. Nalisnick, A. Matsukawa, Y. Whye Teh, D. Gorur, and B. Lakshminarayanan · 2018
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Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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Safer classification by synthesis
W. Wang, A. Wang, A. Tamar, X. Chen, and P. Abbeel · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
E. Wong and J. Z. Kolter · 2018
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Deep anomaly detection with outlier exposure
D. Hendrycks, M. Mazeika, and T. Dietterich · 2019
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Provable certificates for adversarial examples: Fitting a ball in the union of polytopes
M. Jordan, U. Lewis, and A. G. Dimakis · 2019
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Disentangling adversarial robustness and generalization
D. Stutz, M. Hein, and B. Schiele · 2019
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