论文标题
通过残留适应的深度异常检测
Deep Anomaly Detection by Residual Adaptation
论文作者
论文摘要
深度异常检测是一项艰巨的任务，因为在高维度中，只有给出正态性的示例，就很难完全表征“差异”的概念。在本文中，我们提出了一种基于增强大型审计网络的深度异常检测方法的新方法，并使用残留的校正来调整它们，以适应异常检测任务。我们的方法产生了高度参数效率的学习机制，增强了预验证模型中表示形式的分离，并且胜过所有现有的异常检测方法，包括使用预审计的网络（包括其他基线）。例如，在CIFAR-10单一基准测试中，我们的技术将最新技术从96.1提高到99.0均值AUC。
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.
