论文标题
通用数据通过逆生成对手网络检测
Universal Data Anomaly Detection via Inverse Generative Adversary Network
论文作者
论文摘要
考虑检测数据异常的问题。在零假设中，假设模型无异常数据，假定测量值来自具有一些经过认证的历史样本的未知分布。在综合替代假设下，测量来自远离零假设下的分布的未知分布正距离。没有用于分布异常数据的培训数据。提出了基于反向生成对手网络的半监督深度学习技术。
The problem of detecting data anomaly is considered. Under the null hypothesis that models anomaly-free data, measurements are assumed to be from an unknown distribution with some authenticated historical samples. Under the composite alternative hypothesis, measurements are from an unknown distribution positive distance away from the distribution under the null hypothesis. No training data are available for the distribution of anomaly data. A semi-supervised deep learning technique based on an inverse generative adversary network is proposed.
