Out-of-distribution (OOD) detection aims to identify test examples that do not belong to the training distribution and are thus unlikely to be predicted reliably. Despite a plethora of existing works, most of them focused only on the scenario where OOD examples come from semantic shift (e.g., unseen categories), ignoring other possible causes (e.g., covariate shift). In this paper, we present a novel, unifying framework to study OOD detection in a broader scope. Instead of detecting OOD examples from a particular cause, we propose to detect examples that a deployed machine learning model (e.g., an image classifier) is unable to predict correctly. That is, whether a test example should be detected and rejected or not is ``model-specific''. We show that this framework unifies the detection of OOD examples caused by semantic shift and covariate shift, and closely addresses the concern of applying a machine learning model to uncontrolled environments. We provide an extensive analysis that involves a variety of models (e.g., different architectures and training strategies), sources of OOD examples, and OOD detection approaches, and reveal several insights into improving and understanding OOD detection in uncontrolled environments.

翻译：分布外（OOD）检测旨在识别不属于训练分布、因此难以被可靠预测的测试样本。尽管已有大量研究工作，但多数仅聚焦于语义偏移（如未见类别）引发的OOD场景，忽视了其他可能成因（如协变量偏移）。本文提出一种新颖的统一化框架，在更广泛范围内研究OOD检测。我们不针对特定成因检测OOD样本，而是检测部署的机器学习模型（如图像分类器）无法正确预测的样本。也就是说，测试样本是否应被检测并拒绝取决于具体模型。研究表明，该框架统一了语义偏移与协变量偏移导致的OOD检测，并紧密关联了在非受控环境中应用机器学习模型的担忧。通过涵盖多种模型（如不同架构与训练策略）、OOD样本来源及检测方法的广泛分析，我们揭示了在非受控环境中改进与理解OOD检测的多项洞见。