PaperCVPR 2020 · 2020
Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings
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Uninformed Students
Pixel-precise anomaly segmentation from an ensemble of student networks trained only on anomaly-free images to regress a fixed off-domain teacher's dense descriptors, scoring each pixel by regression error against the mixture mean plus the ensemble's predictive variance, across three receptive-field scales.
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Anomaly detection 3
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2019MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly DetectionBergmann, Fauser et al.Built upon by · 4
2022Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and LocalizationBergmann, Batzner et al.2022Towards Total Recall in Industrial Anomaly DetectionRoth, Pemula et al.2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesBatzner, Heckler, König2023Asymmetric Student-Teacher Networks for Industrial Anomaly DetectionRudolph, Wehrbein et al.