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Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies
In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions.
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