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Generative adversarial nets for unsupervised outlier detection
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DOI：10.1016/j.eswa.2023.121161.png)
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Abstract
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Outlier detection, also known as anomaly detection, has been a persistent and active research area for decades due to its wide range of applications in various fields. Many well-established methods have difficulty fitting the distribution of high-dimensional and complex data, making it difficult to detect outliers that have a low degree of deviation. To address this problem, we combine the distribution fitting capability of generative adversarial nets (GANs) with the specificity of the outlier detection problem and propose a GAN-based unsupervised outlier detection (GUOD) method. In a real dataset mixed with normal objects and outliers, the generator reconstruction error of the object is used as its own outlier factor. The top-n objects with the largest reconstruction errors are considered outliers. Extensive ex
Keywords:
ANOMALY-DETECTION
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