Combining pre-trained Vision Transformers and CIDER for Out Of Domain
  Detection
Grégor Jouet Clément Duhart Francis Rousseaux Julio Laborde Cyril de Runz
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