Deep Anomaly Detection by Residual Adaptation

• URL: http://arxiv.org/abs/2010.02310v1
• Date: Mon, 5 Oct 2020 20:02:58 GMT
• Title: Deep Anomaly Detection by Residual Adaptation
• Authors: Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen
• Abstract summary: 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.
• Score: 34.47788988778933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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