论文标题
从基于MIM的GAN到异常检测：事件概率对生成对抗网络的影响
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks
论文作者
论文摘要
为了将深度学习技术引入异常检测，生成的对抗网络（GAN）被认为是算法设计和现实应用中的重要作用。就gan而言，在目标函数中反映的事件概率对事件产生产生了影响，该事件产生在基于GAN的异常检测中起着至关重要的作用。信息指标，例如原始gan中的kullback-leibler差异使目标函数对不同事件概率具有不同的敏感性，这为通过影响数据生成而提供了一个基于GAN的异常检测的机会。在本文中，我们将指数信息指标介绍给GAN，称为基于MIM的GAN，其在理论上讨论了其对数据生成的优越特征。此外，我们提出了一种基于MIM的GAN的异常检测方法，并从概率事件产生的角度解释了无监督学习案例的原理。由于这种方法有望在物联网（IoT）中检测异常（例如环境，医学和生化异常值），因此我们利用来自在线赔率存储库中的几个数据集来评估其性能并将其与其他方法进行比较。
In order to introduce deep learning technologies into anomaly detection, Generative Adversarial Networks (GANs) are considered as important roles in the algorithm design and realistic applications. In terms of GANs, event probability reflected in the objective function, has an impact on the event generation which plays a crucial part in GAN-based anomaly detection. The information metric, e.g. Kullback-Leibler divergence in the original GAN, makes the objective function have different sensitivity on different event probability, which provides an opportunity to refine GAN-based anomaly detection by influencing data generation. In this paper, we introduce the exponential information metric into the GAN, referred to as MIM-based GAN, whose superior characteristics on data generation are discussed in theory. Furthermore, we propose an anomaly detection method with MIM-based GAN, as well as explain its principle for the unsupervised learning case from the viewpoint of probability event generation. Since this method is promising to detect anomalies in Internet of Things (IoT), such as environmental, medical and biochemical outliers, we make use of several datasets from the online ODDS repository to evaluate its performance and compare it with other methods.
