A Dual-Discriminator Generative Adversarial Network for Anomaly Detection

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The dual-discriminator GAN effectively addresses challenges in anomaly detection by better constraining the generator.
Achieving competitive results, the model outperforms several advanced methods in anomaly detection on benchmark datasets.
Generative adversarial networks in this context enhance the quality of reconstruction and accuracy of abnormal data identification.
Retaining information from normal data while filtering out abnormal data is a key benefit of the proposed approach.

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Novel GAN model improves anomaly detection performance in multivariate time series, suggesting practical applications.

Ding et al. (2025) studied this question.

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