Few-Shot Anomaly Detection Using Deep Generative Models for Grouped Data

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Abstract

There exists a great demand for automatic anomaly detection in industrial world. The anomaly has been defined as a group of samples that rarely or never appears. Given a type of products, one has to collect numerous samples and train an anomaly detector. When one diverts a model trained with old types of products with sufficient inventory to the new type, one can detect anomalies of the new type before a production line is established. However, because of the definition of the anomaly, a typical anomaly detector considers the new type of products anomalous even if it is consistent with the standard. Given the above practical demand, this study propose a novel problem setting, few-shot anomaly detection, where an anomaly detector trained in source domains is adapted to a small set of target samples without full retraining. Then, we tackle this problem using a hierarchical probabilistic model based on deep learning. Our empirical results on toy and real-world datasets demonstrate that the proposed model detects anomalies in a small set of target samples successfully.

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Sato, K., Nakata, S., Matsubara, T., & Uehara, K. (2022). Few-Shot Anomaly Detection Using Deep Generative Models for Grouped Data. IEICE Transactions on Information and Systems, E105D(2), 436–440. https://doi.org/10.1587/transinf.2021EDL8063

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