End-to-End Transferable Anomaly Detection via Multi-Spectral Cross-Domain Representation Alignment

Abstract

Anomaly detection (AD) aims to distinguish abnormal instances from what is defined as normal, which strongly correlates with the safe and robust applications of machine learning. A well-performed anomaly detector often relies on the training on massive labeled data, while it is of high cost to annotate data in practice. Fortunately, this dilemma can be solved by transferring the knowledge of a label-rich dataset (source domain) to assist the learning on the label-scarce dataset (target domain), which is known as domain adaptation in transfer learning. In this paper, we propose a Multi-spectral Cross-domain Representation Alignment (MsRA) method for the anomaly detection in the domain adaptation setting, where we can only access normal source data and limited normal target data. Specifically, MsRA first constructs multi-spectral feature representations by fusing different frequency components of the original features, which mitigates the information scarcity due to limited target training data by capturing richer input pattern information. Then we employ the adversarial training strategy to learn domain-invariant features and force the features of normal data to be more compact by the center clustering. Finally, the distance of each sample to the prototype of normal class can be used as its anomaly score, where the prototype is the center of both source and target data. In this way, we achieve anomaly detection in an end-to-end manner, without two-stage training for feature extraction and anomaly detection. Comprehensive experiments on cross-domain anomaly detection benchmarks validate the effectiveness of MsRA.