摘要
基于生成式对抗网络(Generative Adversarial Networks,GAN)的异常检测方法在训练阶段训练集仅由正常数据构成,当训练数据较为充分时,它在该训练集上能够取得较小的重构误差。然而在测试阶段,正常数据的重构误差和部分异常数据的重构误差之间的差别很小,使得基于GAN的异常检测方法的判别性能较差。为了解决该问题,提出了基于记忆增强GAN的异常检测方法。在基于GAN的异常检测方法中加入记忆增强模块,使模型能够记忆正常数据的特征,从而使得异常数据的重构误差变大,该方法的判别性能得到增强。在MNIST,Fashion-MNIST和CIFAR-10上的实验结果表明,与相关方法相比,所提方法具有更优的检测性能。
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 module 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-10verify that the proposed method has better detection performance.
作者
周士金
邢红杰
ZHOU Shi-jin;XING Hong-jie(Hebei Key Laboratory of Machine Learning and Computational Intelligence,College of Mathematics and Information Science,Hebei University,Baoding,Hebei 071002,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S02期261-269,共9页
Computer Science
基金
国家自然科学基金(61672205)
河北省自然科学基金(F2017201020)
河北大学高层次人才科研启动项目(521100222002)
关键词
异常检测
生成式对抗网络
记忆增强
MNIST
Anomaly detection
Generative adversarial networks
Memory-augmented
MNIST
