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Anomaly detection aims to recognize samples with anomalous and unusualpatterns with respect to a set of normal data. This is significant for numerousdomain applications, such as industrial inspection, medical imaging, andsecurity enforcement. There are two key research challenges associated withexisting anomaly detection approaches: (1) many approaches perform well onlow-dimensional problems however the performance on high-dimensional instances,such as images, is limited; (2) many approaches often rely on traditionalsupervised approaches and manual engineering of features, while the topic hasnot been fully explored yet using modern deep learning approaches, even whenthe well-label samples are limited. In this paper, we propose a One-for-allImage Anomaly Detection system (OIAD) based on disentangled learning using onlyclean samples. Our key insight is that the impact of small perturbation on thelatent representation can be bounded for normal samples while anomaly imagesare usually outside such bounded intervals, referred to as structureconsistency. We implement this idea and evaluate its performance for anomalydetection. Our experiments with three datasets show that OIAD can detect over$90\%$ of anomalies while maintaining a low false alarm rate. It can alsodetect suspicious samples from samples labeled as clean, coincided with whathumans would deem unusual.

OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning