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arXiv · 2309.03047

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

Grégor Jouet·Clément Duhart·Francis Rousseaux·Julio Laborde·Cyril de Runz
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Abstract

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

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Grégor Jouet, Clément Duhart, Francis Rousseaux, Julio Laborde, Cyril de Runz. 2023-09-06. Combining pre-trained Vision Transformers and CIDER for Out Of Domain Detection. https://arxiv.org/abs/2309.03047

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