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VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization

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arxiv 2104.10036 v1 pith:ZBSGA2U5 submitted 2021-04-20 cs.CV cs.AIcs.LG

Pankaj Mishra , Riccardo Verk , Daniele Fornasier , Claudio Piciarelli , Gian Luca Foresti This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords anomalynetworkdetectionimagelocalizationtransformeradditionalgorithms
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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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  1. Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model

    cs.LG 2025-01 conditional novelty 7.0 of 10

    A solvable teacher-student perceptron model predicts that the optimal fraction of anomaly examples in training is generally away from 50%, with a sharp crossover as training noise increases.

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