Abstract
Image anomaly detection has recently emerged a large number of methods, which are widely used in industry, medicine and other fields. In this paper, we propose a novel image anomaly detection method, named Ensemble of One-Class Classifiers based on multi-level hidden representations abstracted from convolutional autoencoder (EOCCA), which is a two-stage method. Specifically, in the training process of the model, first, we train the convolutional autoencoder with the goal of minimizing the reconstruction error, thereby extracting the features from different layers of the encoder. Second, we train multiple one-class support vector machines based on the multi-level hidden representations abstracted from convolutional autoencoder. During inference, we ensemble all trained one-class classifiers for anomaly detection. Experimental results on several image anomaly detection benchmark datasets demonstrate that our proposed method is on par or outperforms current state-of-the-art image anomaly detection methods.
X. Wang and J. Liu—This work was supported by the Science Foundation of China. University of Petroleum, Beijing (No.2462020YXZZ023).
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Wang, Xt., Liu, Jw. (2022). Ensemble of One-Class Classifiers Based on Multi-level Hidden Representations Abstracted from Convolutional Autoencoder for Anomaly Detection. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13531. Springer, Cham. https://doi.org/10.1007/978-3-031-15934-3_11
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