DAVIDSTUTZ

Check out our latest research on adversarial robustness and generalization of deep networks.
17thAUGUST2018

Gilmer et al. study the existence of adversarial examples on a synthetic toy datasets consisting of two concentric spheres. The dataset is created by randomly sampling examples from two concentric spheres, one with radius $1$ and one with radius $R = 1.3$. While the authors argue that difference difficulties of the dataset can be created by varying $R$ and the dimensionality, they merely experiment with $R = 1.3$ and a dimensionality of $500$. The motivation to study this dataset comes form the idea that adversarial examples can easily be found by leaving the data manifold. Based on this simple dataset, the authors provide several theoretical insights – see the paper for details.