When adversarial examples are excusable. (arXiv:2204.11985v1 [cs.LG])

Neural networks work remarkably well in practice and theoretically they can
be universal approximators. However, they still make mistakes and a specific
type of them called adversarial errors seem inexcusable to humans. In this
work, we analyze both test errors and adversarial errors on a well controlled
but highly non-linear visual classification problem. We find that, when
approximating training on infinite data, test errors tend to be close to the
ground truth decision boundary. Qualitatively speaking these are also more …

adversarial lg