Out-of-domain (OOD) detection is a crucial component in industrial applications as it helps identify when a model encounters inputs that are outside the training distribution. Most industrial pipelines rely on pre-trained models for downstream tasks such as CNN or Vision Transformers. This paper investigates the performance of those models on the task of out-of-domain detection. Our experiments demonstrate that pre-trained transformers models achieve higher detection performance out of the box. Furthermore, we show that pre-trained ViT and CNNs can be combined with refinement methods such as CIDER to improve their OOD detection performance even more. Our results suggest that transformers are a promising approach for OOD detection and set a stronger baseline for this task in many contexts

翻译：域外检测是工业应用中的关键组成部分，它有助于识别模型何时遇到训练分布之外的输入。大多数工业流程依赖预训练模型（如CNN或视觉Transformer）处理下游任务。本文研究了这些模型在域外检测任务上的表现。实验表明，预训练的Transformer模型在开箱即用的情况下能实现更高的检测性能。此外，我们展示了预训练的ViT和CNN可与CIDER等精炼方法结合使用，以进一步提升其域外检测性能。我们的结果表明，Transformer是域外检测中一种有前景的方法，并为该任务在许多场景下建立了更强的基线。