  Anomaly detection suffered from the lack of anomalies due to the diversity of
abnormalities and the difficulties of obtaining large-scale anomaly data.
Semi-supervised anomaly detection methods are often used to solely leverage
normal data to detect abnormalities that deviated from the learnt normality
distributions. Meanwhile, given the fact that limited anomaly data can be
obtained with a minor cost in practice, some researches also investigated
anomaly detection methods under supervised scenarios with limited anomaly data.
In order to address the lack of abnormal data for robust anomaly detection, we
propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based
anomaly detection paradigm that learns to detect anomalies by generating
\textit{contextual adversarial information} from the massive normal examples.
Essentially, our method generates pseudo-anomaly data for both supervised and
semi-supervised anomaly detection scenarios. Extensive experiments are carried
out on multiple benchmark datasets and real-world datasets, the results show
significant improvement in both supervised and semi-supervised scenarios.
Importantly, our approach is data-efficient that can boost up the detection
accuracy with no more than 5% anomalous training data.
