Food: Fast out-of-distribution detector

Guy Amit, Moshe Levy, Ishai Rosenberg, Asaf Shabtai, Yuval Elovici

2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021

Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in-distribution inputs. However, out-of-distribution (OOD) inputs pose a great challenge to DNNs and consequently represent a major risk when DNNs are implemented in safety-critical systems. Extensive research has been performed in the domain of OOD detection. However, current state-of-the-art methods for OOD detection suffer from at least one of the following limitations: (1) increased inference time – this limits existing methods’ applicability to many real-world applications, and (2) the need for OOD training data – such data can be difficult to acquire and may not be representative enough, thus limiting the ability of the OOD detector to generalize. In this paper, we propose FOOD – Fast Out-Of-Distribution detector – an extended DNN classifier capable of efficiently detecting …