  We investigate the problems and challenges of evaluating the robustness of
Differential Equation-based (DE) networks against synthetic distribution
shifts. We propose a novel and simple accuracy metric which can be used to
evaluate intrinsic robustness and to validate dataset corruption simulators. We
also propose methodology recommendations, destined for evaluating the many
faces of neural DEs' robustness and for comparing them with their discrete
counterparts rigorously. We then use this criteria to evaluate a cheap data
augmentation technique as a reliable way for demonstrating the natural
robustness of neural ODEs against simulated image corruptions across multiple
datasets.
