  Previous transfer methods for anomaly detection generally assume the
availability of labeled data in source or target domains. However, such an
assumption is not valid in most real applications where large-scale labeled
data are too expensive. Therefore, this paper proposes an importance weighted
adversarial autoencoder-based method to transfer anomaly detection knowledge in
an unsupervised manner, particularly for a rarely studied scenario where a
target domain has no labeled normal/abnormal data while only normal data from a
related source domain exist. Specifically, the method learns to align the
distributions of normal data in both source and target domains, but leave the
distribution of abnormal data in the target domain unchanged. In this way, an
obvious gap can be produced between the distributions of normal and abnormal
data in the target domain, therefore enabling the anomaly detection in the
domain. Extensive experiments on multiple synthetic datasets and the UCSD
benchmark demonstrate the effectiveness of our approach.
