  Deep neural networks suffer from the overconfidence issue in the open world,
meaning that classifiers could yield confident, incorrect predictions for
out-of-distribution (OOD) samples. Thus, it is an urgent and challenging task
to detect these samples drawn far away from training distribution based on the
security considerations of artificial intelligence. Many current methods based
on neural networks mainly rely on complex processing strategies, such as
temperature scaling and input preprocessing, to obtain satisfactory results. In
this paper, we propose an effective algorithm for detecting out-of-distribution
examples utilizing PEDCC-Loss. We mathematically analyze the nature of the
confidence score output by the PEDCC (Predefined Evenly-Distribution Class
Centroids) classifier, and then construct a more effective scoring function to
distinguish in-distribution (ID) and out-of-distribution. In this method, there
is no need to preprocess the input samples and the computational burden of the
algorithm is reduced. Experiments demonstrate that our method can achieve
better OOD detection performance.
