基于记忆增强 GAN 的异常检测

doi:10.11896/jsjkx.211000202

Abstract

Abstract: In the training stage of the generative adversarial networks(GAN) based anomaly detection method,its training set consists of only normal data.When training data are sufficient,the GAN based anomaly detection method may obtain smaller reconstruction error.However,in the testing stage,the difference between the reconstruction errors of normal data and those of part novel data is too small,which makes the discriminant performance of the GAN based anomaly detection method become poor.To solve this problem,a memory-augmented GAN based anomaly detection method is proposed.A memory-augmented mo-dule is introduced into the proposed method to make it remember the characteristic of normal data.Hence,the reconstruction error of novel data becomes larger and thus the discriminant ability of the proposed method is enhanced.In comparison with the related approaches,experimental results on MNIST,Fashion-MNIST and CIFAR-10 verify that the proposed method has better detection performance.

Key words: Anomaly detection, Generative adversarial networks, Memory-augmented, MNIST

CLC Number:

TP391.4

ZHOU Shi-jin, XING Hong-jieHebei. Memory-augmented GAN-based Anomaly Detection[J].Computer Science, 2022, 49(11A): 211000202-9.

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