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Fence GAN: Towards Better Anomaly Detection
[article]
2019
arXiv
pre-print
Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has
arXiv:1904.01209v1
fatcat:d445h7p675bxdofxif24iu3mia