Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen before and cannot make a safe decision. This problem first emerged in 2017 and since then has received increasing attention from the research community, leading to a plethora of methods developed, ranging from classification-based to density-based to distance-based ones. Meanwhile, several other problems are closely related to OOD detection in terms of motivation and methodology. These include anomaly detection (AD), novelty detection (ND), open set recognition (OSR), and outlier detection (OD). Despite having different definitions and problem settings, these problems often confuse readers and practitioners, and as a result, some existing studies misuse terms. In this survey, we first present a generic framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD detection, and OD. Under our framework, these five problems can be seen as special cases or sub-tasks, and are easier to distinguish. Then, we conduct a thorough review of each of the five areas by summarizing their recent technical developments. We conclude this survey with open challenges and potential research directions.

翻译：例如,在自主驾驶中,我们希望驾驶系统在发现以前从未见过的异常场景或物体时,向人类发出警报,并将控制权交给发现异常场景或物体的人,因为人类从未见过异常场景或物体,因此无法作出安全的决定。2017年首次出现这一问题,此后研究界日益关注这一问题,导致开发了大量方法,从基于分类的检测到基于密度的检测到基于远程的检测。与此同时,在动力和方法方面,其他一些问题与OOOD的检测密切相关,其中包括异常检测(AD)、新发现(ND)、开放确认(OSR)和外部检测(OD)。尽管存在不同的定义和问题设置,但这些问题往往使读者和从业人员感到困惑,并因此,一些现有的研究误用术语。在本次调查中,我们首先提出了一个通用的框架,称为通用的OOD检测,其中包括上述五个问题,即AD、ND、OSR、OD的检测和OD。在我们的框架下,这五个问题可以被视为一个特殊的案例,或分层研究领域,我们通过对每个技术领域进行更加容易的审视。