On the Brittleness of Robust Features: An Exploratory Analysis of Model Robustness and Illusionary Robust Features KU Leuven
Neural networks have been shown to be vulnerable to visual data perturbations imperceptible to the human eye. Nowadays, the leading hypothesis about the reason for the existence of these adversarial examples is the presence of non-robust features, which are highly predictive but brittle. Also, it has been shown that there exist two types of non-robust features depending on whether or not they are entangled with robust features; perturbing ...