Fence GAN: Towards Better Anomaly Detection

1 code implementation • 2 Apr 2019 • Cuong Phuc Ngo, Amadeus Aristo Winarto, Connie Kou Khor Li, Sojeong Park, Farhan Akram, Hwee Kuan Lee

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 been found to be ineffective as an anomaly detector.

Anomaly Classification Anomaly Detection +1