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
@article{arxiv.2309.03047,
title = {Combining pre-trained Vision Transformers and CIDER for Out Of Domain Detection},
author = {Grégor Jouet and Clément Duhart and Francis Rousseaux and Julio Laborde and Cyril de Runz},
journal= {arXiv preprint arXiv:2309.03047},
year = {2023}
}