Anomaly Detection with Domain Adaptation
This addresses the problem of adapting anomaly detectors across domains for applications like image analysis, but it is incremental as it builds on existing domain adaptation and anomaly detection methods.
The paper tackles semi-supervised anomaly detection with domain adaptation by proposing IRAD, which learns domain-invariant representations using adversarial learning, and shows it outperforms baseline models by a wide margin on digits and object recognition datasets.
We study the problem of semi-supervised anomaly detection with domain adaptation. Given a set of normal data from a source domain and a limited amount of normal examples from a target domain, the goal is to have a well-performing anomaly detector in the target domain. We propose the Invariant Representation Anomaly Detection (IRAD) to solve this problem where we first learn to extract a domain-invariant representation. The extraction is achieved by an across-domain encoder trained together with source-specific encoders and generators by adversarial learning. An anomaly detector is then trained using the learnt representations. We evaluate IRAD extensively on digits images datasets (MNIST, USPS and SVHN) and object recognition datasets (Office-Home). Experimental results show that IRAD outperforms baseline models by a wide margin across different datasets. We derive a theoretical lower bound for the joint error that explains the performance decay from overtraining and also an upper bound for the generalization error.