VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps to preserve the spatial information of the embedded patches, which are later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec.
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Tasks
Datasets
Introduced in the Paper:
BTAD
Results from the Paper
Ranked #9 on
Anomaly Detection
on BTAD
(using extra training data)
