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

Combining pre-trained Vision Transformers and CIDER for Out Of Domain Detection