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Adversarial Training with Voronoi Constraints
[article]
2019
arXiv
pre-print
Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-dimensional geometry of adversarial examples. In particular, we highlight the importance of codimension: for low-dimensional data manifolds embedded in high-dimensional space
arXiv:1905.01019v1
fatcat:fldbvjrhknfudf7vxchhqj7kqe