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We investigate the problems and challenges of evaluating the robustness ofDifferential Equation-based (DE) networks against synthetic distributionshifts. We propose a novel and simple accuracy metric which can be used toevaluate intrinsic robustness and to validate dataset corruption simulators. Wealso propose methodology recommendations, destined for evaluating the manyfaces of neural DEs' robustness and for comparing them with their discretecounterparts rigorously. We then use this criteria to evaluate a cheap dataaugmentation technique as a reliable way for demonstrating the naturalrobustness of neural ODEs against simulated image corruptions across multipledatasets.

Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening