Quantifying Lower Reliability Bounds of Deep Neural Networks
Max Scheerer, Marius Take, Jonas Klamroth · 2024
Deep Neural Networks (DNNs) continue to permeate various domains, including mission-critical systems. Their unverifiable nature, however, makes them inherently unreliable such that reliability quantification becomes essential to determine whether a DNN is suitable for operation. In this paper, we approach this problem by quantifying lower reliability bounds of DNNs. Our approach builds upon the widely known Conformal Predicition framework for determining reliability and discusses a sampling procedure for approximating the lower bound of reliability, leveraging properties of the data manifold. In our evaluation, we demonstrate the plausibility of the approximated reliability bounds and also show that incorrect predictions correlate with low reliability. The main benefits of our approach are that it enables the evaluation and comparison of DNNs in terms of their reliability on the one hand, and provides a way to incorporate them in model-based reliability analyses at the system level on the other.