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. Further, we propose to directly estimate the density ratio of a data sample through class ratio estimation. We report competitive results on OOD image problems in comparison with recent work that alternatively requires training of deep generative models for the task. Our approach enables a simple and yet effective path towards solving the OOD detection problem.
翻译:最近发现,在探测OOD图像的任务中,基于密度比率的检测不可靠,各种基于密度比率的方法都取得了良好的实证业绩,但方法通常缺乏有原则的概率模型解释。在这项工作中,我们提议将基于密度比率的方法统一在一个新框架之下,以建立基于能源的模型并利用不同的基分布。在我们的框架内,密度比率可被视为隐含的语义分布的不正规密度。此外,我们提议通过类比估计直接估计数据样本的密度比率。我们报告OOOD图像问题的竞争性结果,而最近的工作则要求为这项任务培训深层基因模型。我们的方法为解决OOD探测问题提供了简单而有效的途径。