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In the context of flexible manufacturing systems that are required to produce different types and quantities of products with minimal reconfiguration, this paper addresses the problem of unsupervised multi-class anomaly detection: develop a unified model to detect anomalies from objects belonging to multiple classes when only normal data is accessible.
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Using pre-training can improve model robustness and uncertainty
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Transfer learning gaussian anomaly detection by fine-tuning representations
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Modeling the distribution of normal data in pre-trained deep features for anomaly detection
Oliver Rippel, Patrick Mertens, and Dorit Merhof · 2021
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Multiresolution knowledge distillation for anomaly detection
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Unsupervised anomaly segmentation via deep feature reconstruction
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