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• W3091874484 abstract "Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of differentness when given only examples of normality. In this paper we propose a novel approach to deep anomaly detection based on augmenting large pretrained networks with residual corrections that adjusts them to the task of anomaly detection. Our method gives rise to a highly parameter-efficient learning mechanism, enhances disentanglement of representations in the pretrained model, and outperforms all existing anomaly detection methods including other baselines utilizing pretrained networks. On the CIFAR-10 one-versus-rest benchmark, for example, our technique raises the state of the art from 96.1 to 99.0 mean AUC." @default.
• W3091874484 created "2020-10-15" @default.
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• W3091874484 date "2020-10-05" @default.
• W3091874484 modified "2023-10-01" @default.
• W3091874484 title "Deep Anomaly Detection by Residual Adaptation." @default.
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• W3091874484 hasPublicationYear "2020" @default.
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