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OOD Detection with Class Ratio Estimation
Mingtian Zhang · Andi Zhang · Tim Xiao · Yitong Sun · Steven McDonagh

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in Workshop on Machine Learning Safety »

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.

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Author Information

Mingtian Zhang (UCL)
Andi Zhang (University of Cambridge)
Tim Xiao (University of Tuebingen)
Yitong Sun (Huawei Noah's Ark Lab)
Steven McDonagh (Huawei Noah's Ark Lab)

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