Unsupervised Anomaly Detection Under A Multiple Modeling Strategy Via Model Set Optimization Through Transfer Learning

Publication Date: 6/30/2023

Event: The 26th International Conference on Information Fusion, Charleston, SC

Reference: pp. 1-8, 2023

Authors: Masanao Natsumeda, NEC Corporation; Takehiko Mizoguchi, NEC Corporation; Wei Cheng, NEC Laboratories America, Inc.; Yuncong Chen, NEC Laboratories America, Inc.; Haifeng Chen, NEC Laboratories America, Inc.

Abstract: Unsupervised anomaly detection approaches have been widely accepted in applications for industrial systems. Industrial systems often operate with multiple modes since they work for multiple purposes or under different conditions. In order to deal with the difficulty of anomaly detection due to multiple operating modes, multiple modeling strategies are employed. However, estimating the optimal set of models is a challenging problem due to the lack of supervision and computational burden. In this paper, we propose DeconAnomaly, a deep learning framework to estimate the optimal set of models using transfer learning for unsupervised anomaly detection under a multiple modeling strategy. It reduces computational burden with transfer learning and optimizes the number of models based on a surrogate metric of detection performance. The experimental results show clear advantages of DeconAnomaly.

Publication Link: https://ieeexplore.ieee.org/document/10224107