Out-of-Distribution Detection with Class Ratio Estimation

Understand

Density-based Out-of-distribution (OOD) detection has recently been shown unreliable for the task of detecting OOD images.

• Various density ratio based approaches achieve good empirical performance, however methods typically lack a principled probabilistic modelling explanation.
• In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.
• Under our framework, the density ratio can be viewed as the unnormalized density of an implicit semantic distribution.