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Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not recognized by humans.
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Anders Krogh and Jesper Vedelsby · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
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Earlier work this paper cites.
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Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Improving adversarial robustness requires revisiting misclassified examples
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