Outlier detection tasks have been playing a critical role in AI safety. There has been a great challenge to deal with this task. Observations show that deep neural network classifiers usually tend to incorrectly classify out-of-distribution (OOD) inputs into in-distribution classes with high confidence. Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training. In this paper, we propose an alternative probabilistic paradigm that is both practically useful and theoretically viable for the OOD detection tasks. Particularly, we impose statistical independence between inlier and outlier data during training, in order to ensure that inlier data reveals little information about OOD data to the deep estimator during training. Specifically, we estimate the statistical dependence between inlier and outlier data through the Hilbert-Schmidt Independence Criterion (HSIC), and we penalize such metric during training. We also associate our approach with a novel statistical test during the inference time coupled with our principled motivation. Empirical results show that our method is effective and robust for OOD detection on various benchmarks. In comparison to SOTA models, our approach achieves significant improvement regarding FPR95, AUROC, and AUPR metrics. Code is available: \url{https://github.com/jylins/hood}.

翻译：外部探测任务在AI安全方面一直发挥着关键作用。 处理这项任务一直面临巨大的挑战。 观察显示,深神经网络分类人员通常会非常自信地错误地将分配外(OOOD)投入分类到分配类中。 现有工作试图解决这个问题,在OOOD投入在培训期间暴露在分类人员面前时,明确将不确定性强加给分类人员。 在本文件中,我们提出了一种替代的概率性模式,这种模式对OOOD检测任务实际上有用,在理论上也可行。 特别是,我们在培训期间将内和外的数据在统计上的独立性强加于人,以确保在培训期间向深度估计者披露关于OOD数据的信息很少。 具体地说,我们通过Hilbert-Schmidt 独立性标准(HSIC)来估计离异和外部数据之间的统计依赖性,我们在培训期间惩罚这种指标。 我们还将我们的方法与在推断期间进行的新统计测试以及我们的原则性动机联系起来。 Emprical结果显示,我们在各种基准上对ODUR检测的方法是有效和健全的。 与SOD/RUPRM 。 在与SUDAtal/RMA方面, 我们的方法是显著的改进。