论文标题
记忆增强生成的对抗网络用于异常检测
Memory Augmented Generative Adversarial Networks for Anomaly Detection
论文作者
论文摘要
在本文中，我们提出了一种用于异常检测的内存调节算法。经典的异常检测算法专注于学习建模和生成正常数据，但通常保证检测异常数据的数据较弱。提出的内存增强生成对抗网络（孟加斯）与编码过程和生成过程都与存储模块进行交互。我们的算法使得大多数\ textit {编码}正常数据位于内存单元的凸面内部，而异常数据则在外部隔离。如此出色的特性导致正常数据（分别\ fribnormal）数据的良好（分别\差）重建，因此为异常检测提供了强有力的保证。与以前的方法相比，孟加斯中解码的内存单元更容易解释和分解，这进一步证明了记忆机制的有效性。 CIFAR-10和MNIST的二十个异常检测数据集的实验结果表明，孟加斯表现出比以前的异常检测方法的显着改善。
In this paper, we present a memory-augmented algorithm for anomaly detection. Classical anomaly detection algorithms focus on learning to model and generate normal data, but typically guarantees for detecting anomalous data are weak. The proposed Memory Augmented Generative Adversarial Networks (MEMGAN) interacts with a memory module for both the encoding and generation processes. Our algorithm is such that most of the \textit{encoded} normal data are inside the convex hull of the memory units, while the abnormal data are isolated outside. Such a remarkable property leads to good (resp.\ poor) reconstruction for normal (resp.\ abnormal) data and therefore provides a strong guarantee for anomaly detection. Decoded memory units in MEMGAN are more interpretable and disentangled than previous methods, which further demonstrates the effectiveness of the memory mechanism. Experimental results on twenty anomaly detection datasets of CIFAR-10 and MNIST show that MEMGAN demonstrates significant improvements over previous anomaly detection methods.
