on the effectiveness of generative adversarial network on anomaly detection. (arXiv:2112.15541v1 [cs.LG])

Identifying anomalies refers to detecting samples that do not resemble the
training data distribution. Many generative models have been used to find
anomalies, and among them, generative adversarial network (GAN)-based
approaches are currently very popular. GANs mainly rely on the rich contextual
information of these models to identify the actual training distribution.
Following this analogy, we suggested a new unsupervised model based on GANs --a
combination of an autoencoder and a GAN. Further, a new scoring function was
introduced to …

anomaly detection arxiv detection generative adversarial network