From MIM-Based GAN to Anomaly Detection:Event Probability Influence on Generative Adversarial Networks. (arXiv:2203.13464v1 [cs.LG])

In order to introduce deep learning technologies into anomaly detection,
Generative Adversarial Networks (GANs) are considered as important roles in the
algorithm design and realistic applications. In terms of GANs, event
probability reflected in the objective function, has an impact on the event
generation which plays a crucial part in GAN-based anomaly detection. The
information metric, e.g. Kullback-Leibler divergence in the original GAN, makes
the objective function have different sensitivity on different event
probability, which provides an opportunity to refine …

anomaly detection arxiv detection event gan generative adversarial networks influence networks probability