  The capability of reliably detecting out-of-distribution samples is one of
the key factors in deploying a good classifier, as the test distribution always
does not match with the training distribution in most real-world applications.
In this work, we present a deep multi-class data description, termed as
Deep-MCDD, which is effective to detect out-of-distribution (OOD) samples as
well as classify in-distribution (ID) samples. Unlike the softmax classifier
that only focuses on the linear decision boundary partitioning its latent space
into multiple regions, our Deep-MCDD aims to find a spherical decision boundary
for each class which determines whether a test sample belongs to the class or
not. By integrating the concept of Gaussian discriminant analysis into deep
neural networks, we propose a deep learning objective to learn
class-conditional distributions that are explicitly modeled as separable
Gaussian distributions. Thereby, we can define the confidence score by the
distance of a test sample from each class-conditional distribution, and utilize
it for identifying OOD samples. Our empirical evaluation on multi-class tabular
and image datasets demonstrates that Deep-MCDD achieves the best performances
in distinguishing OOD samples while showing the classification accuracy as high
as the other competitors.
