Deep Anomaly Detection by Residual Adaptation

Lucas Deecke; Lukas Ruff; Robert A. Vandermeulen; Hakan Bilen

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arXiv · 2010.02310

Deep Anomaly Detection by Residual Adaptation

Lucas Deecke·Lukas Ruff·Robert A. Vandermeulen·Hakan Bilen

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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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Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen. 2020-10-05. Deep Anomaly Detection by Residual Adaptation. https://arxiv.org/abs/2010.02310

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