Adversarial Robustness on In- and Out-Distribution Improves Explainability-Reference-Cited by-同舟云学术

Adversarial Robustness on In- and Out-Distribution Improves Explainability

Published:2020 Issue: Volume: Page:228-245
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2020
language:
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Author:

Augustin Maximilian,Meinke Alexander,Hein Matthias

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58574-7_14

Reference61 articles.

1. Alayrac, J.B., Uesato, J., Huang, P.S., Fawzi, A., Stanforth, R., Kohli, P.: Are labels required for improving adversarial robustness? In: NeurIPS (2019)

2. Andriushchenko, M., Croce, F., Flammarion, N., Hein, M.: Square attack: a query-efficient black-box adversarial attack via random search. In: ECCV (2020)

3. Athalye, A., Carlini, N., Wagner, D.A.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In: ICML (2018)

4. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)

5. Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., Müller, K.R.: How to explain individual classification decisions. J. Mach. Learn. Res. (JMLR) 11, 1803–1831 (2010)

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