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• W3204332276 abstract "We introduce a new self-supervised task, NSA, for training an end-to-end model for anomaly detection and localization using only normal data. NSA uses Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects. Our method achieves an overall detection AUROC of 97.2 outperforming all previous methods that learn from scratch without pre-training datasets." @default.
• W3204332276 created "2021-10-11" @default.
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• W3204332276 date "2021-09-30" @default.
• W3204332276 modified "2023-09-27" @default.
• W3204332276 title "Self-Supervised Out-of-Distribution Detection and Localization with Natural Synthetic Anomalies (NSA)." @default.
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• W3204332276 hasPublicationYear "2021" @default.
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