OOD Detection with Class Ratio Estimation

Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun, Steven McDonagh

Abstract:

Density-based Out-of-distribution (OOD) detection has recently been shown
unreliable for the task of detecting OOD images. Various density ratio based approaches have achieved good empirical performance. However, these methods typically lack a principled probabilistic modelling explanation. In this work, we propose to unify density ratio based methods under a novel energy-based model framework that allows us to view the density ratio as the unnormalized density of an implicit semantic distribution. Further, we propose to directly estimate the density ratio of a data sample through class ratio estimation, which can achieve competitive OOD detection results without training any deep generative models. Our approach enables a simple yet effective path towards solving OOD detection problems in the image domain.