Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label significantly compromises the reliability of a model. The problem has gained significant attention due to its importance for safety deploying models in open-world settings. Detecting OOD samples is challenging due to the intractability of modeling all possible unknown distributions. To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection. Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently. Accordingly, these research avenues have not cross-pollinated, creating research barriers. While some surveys intend to provide an overview of these approaches, they seem to only focus on a specific domain without examining the relationship between different domains. This survey aims to provide a cross-domain and comprehensive review of numerous eminent works in respective areas while identifying their commonalities. Researchers can benefit from the overview of research advances in different fields and develop future methodology synergistically. Furthermore, to the best of our knowledge, while there are surveys in anomaly detection or one-class learning, there is no comprehensive or up-to-date survey on out-of-distribution detection, which our survey covers extensively. Finally, having a unified cross-domain perspective, we discuss and shed light on future lines of research, intending to bring these fields closer together.
翻译:机器学习模型往往遇到不同于培训分布的样本。 不承认分配外(OOOD)样本,因此将样本划为类内标签,会大大损害模型的可靠性。 这一问题由于安全在开放世界环境中部署模型的重要性而引起极大关注。 检测OOOD样本具有挑战性,因为所有可能的未知分布模式的建模不易。 迄今,若干研究领域解决了探测不熟悉样本的问题,包括异常检测、新发现、一等学习、公开确认和分配外探测。 尽管存在类似和共享的概念,但分配外、公开设定和异常检测却大大削弱了模型的可靠性。 因此,这些研究渠道没有交叉污染,造成了研究障碍。 尽管有些调查打算概述这些方法,但似乎仅仅侧重于一个特定领域,而没有研究不同领域之间的关系。 本次调查的目的是对不同领域的众多知名作品进行交叉和全面审查,同时查明它们的共性。 研究人员可以从不同领域的深入研究进展概览中获益于不同领域进行更深入的研究进展的概览,在进行更深入的实地调查或未来的方法上进行更深入的考察,而我们则从一个领域进行更深入的实地的考察,最终的考察。