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Memory enhancement method based on Skip-GANomaly for anomaly detection

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Abstract

Unsupervised anomaly detection methods based on the Generative Adversarial Network have solved the problem of supervised anomaly detection methods relying on the training of known anomaly samples. However, the encoder is so powerful as to reconstruct the abnormal samples well, eventually resulting in missed detection. To address this problem, this paper proposes a memory enhancement method based on Skip-GANomaly for anomaly detection to optimize the generator. During the training process, the number of skip-connection layers is adjusted to reduce the generator overfitting. The coded information is used as query in the memory enhancement module attention to retrieve the most relevant items in the module memory and the memory content can be updated and frozen, so as to improve the reliability of the coding information, increase the error of anomaly reconstruction, finally to improve the detection accuracy. The average AUC values of this method tested on the public datasets MNIST and CIFAR-10 reach 0.905 and 0.851, improved by 15.0% and 12.0%, respectively. It fully indicates the versatility and effectiveness of the method described in this paper.

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Data Availability

The data generated in the course of the current study may be obtained from the corresponding author on reasonable request.

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Funding

National Natural Science Foundation of China(61960206010).

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Authors and Affiliations

  1. School of Physical Science and Technology, Institute of Optoelectronic Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Wan Jiang, Kai Yang, Chunrong Qiu & Liming Xie

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  1. Wan Jiang
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  2. Kai Yang
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  3. Chunrong Qiu
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  4. Liming Xie
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Correspondence to Wan Jiang.

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Jiang, W., Yang, K., Qiu, C. et al. Memory enhancement method based on Skip-GANomaly for anomaly detection. Multimed Tools Appl 83, 19501–19516 (2024). https://doi.org/10.1007/s11042-023-16317-6

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