2023/09/06 by Grégor Jouet, Clément Duhart, Jouet, Grégor +7
Engineering · Computer Science · #Non-Destructive Testing Techniques #Advanced Neural Network Applications #Industrial Vision Systems and Defect Detection
paper · pdf · doi:10.48550/arxiv.2309.03047
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