Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most popular state-of-the-art methods are unable to consistently outperform a simple and relatively unknown anomaly detector based on the Mahalanobis distance (MahaAD). A key reason for the inconsistencies of these methods is the lack of a formal description of U-OOD. Motivated by a simple thought experiment, we propose a characterization of U-OOD based on the invariants of the training dataset. We show how this characterization is unknowingly embodied in the top-scoring MahaAD method, thereby explaining its quality. Furthermore, our approach can be used to interpret predictions of U-OOD detectors and provides insights into good practices for evaluating future U-OOD methods.

翻译：最近,由于在任务关键系统中的重要性和对受监督对应方的更广泛适用性,无人监督的分布外探测(U-OOOD)最近引起许多注意。尽管注意程度增加,但U-OOOD方法存在重大缺陷。我们通过对不同基准和图像模式进行大规模评价,在这项工作中显示,最受欢迎的最先进方法无法始终超越基于Mahalanobis距离(MahaAD)的简单和相对不为人所知的异常探测器。这些方法不一致的一个关键原因是缺乏对U-OOD的正式描述。我们受到简单思考实验的驱动,我们根据培训数据集的变量,提出了对U-OOD的定性。我们展示了这种特征如何不知情地体现在最尖端的MahaAD方法中,从而解释了其质量。此外,我们的方法可以用来解释对U-OOD探测器的预测,并为评估未来U-OOD方法的好做法提供见解。