On the Geometry of Adversarial ExamplesOn the Geometry of Adversarial ExamplesKhoury, Marc and Hadfield-Menell, Dylan2018
Paper summarydavidstutzKhoury and Hadfield-Menell provide two important theoretical insights regarding adversarial robustness: it is impossible to be robust in terms of all norms, and adversarial training is sample inefficient. Specifically, they study robustness in relation to the problem’s codimension, i.e., the difference between the dimensionality of the embedding space (e.g., image space) and the dimensionality of the manifold (where the data is assumed to actually live on). Then, adversarial training is shown to be sample inefficient in high codimensions.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
Khoury and Hadfield-Menell provide two important theoretical insights regarding adversarial robustness: it is impossible to be robust in terms of all norms, and adversarial training is sample inefficient. Specifically, they study robustness in relation to the problem’s codimension, i.e., the difference between the dimensionality of the embedding space (e.g., image space) and the dimensionality of the manifold (where the data is assumed to actually live on). Then, adversarial training is shown to be sample inefficient in high codimensions.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).