Improved AutoEncoder With LSTM Module and KL Divergence for Anomaly Detection
The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description (SVDD) have been universally used and have demonstrated significant success in detecting anomalies. However, the o...
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| Vydané v: | IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 11 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description (SVDD) have been universally used and have demonstrated significant success in detecting anomalies. However, the over-reconstruction ability of CAE network for anomalous data can easily lead to high false-negative rate in detecting anomalous data. On the other hand, the deep support vector data description (Deep SVDD) model has the drawback of feature collapse, which leads to a decrease in detection accuracy for anomalies. To address these problems, we propose the Improved AutoEncoder with LSTM module and Kullback-Leibler divergence (IAE-LSTM-KL) model in this article. An LSTM network is added after the encoder to memorize feature representations of normal data. Meanwhile, the phenomenon of feature collapse can also be mitigated by penalizing the featured input to SVDD module via KL divergence. The efficacy of the IAE-LSTM-KL model is validated through experiments on both synthetic and real-world datasets. Experimental results show that IAE-LSTM-KL model yields higher detection accuracy for anomalies. In addition, it is also found that the IAE-LSTM-KL model demonstrates enhanced robustness to contaminated outliers in the dataset. |
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| AbstractList | The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description (SVDD) have been universally used and have demonstrated significant success in detecting anomalies. However, the over-reconstruction ability of CAE network for anomalous data can easily lead to high false-negative rate in detecting anomalous data. On the other hand, the deep support vector data description (Deep SVDD) model has the drawback of feature collapse, which leads to a decrease in detection accuracy for anomalies. To address these problems, we propose the Improved AutoEncoder with LSTM module and Kullback-Leibler divergence (IAE-LSTM-KL) model in this article. An LSTM network is added after the encoder to memorize feature representations of normal data. Meanwhile, the phenomenon of feature collapse can also be mitigated by penalizing the featured input to SVDD module via KL divergence. The efficacy of the IAE-LSTM-KL model is validated through experiments on both synthetic and real-world datasets. Experimental results show that IAE-LSTM-KL model yields higher detection accuracy for anomalies. In addition, it is also found that the IAE-LSTM-KL model demonstrates enhanced robustness to contaminated outliers in the dataset. |
| Author | Wan, Rongchun Zhang, Kaituo Zhang, Bingyang Gao, Hua Huang, Wei |
| Author_xml | – sequence: 1 givenname: Wei orcidid: 0000-0002-6684-5642 surname: Huang fullname: Huang, Wei organization: College of Computer Science, Zhejiang University of Technology, Hangzhou, China – sequence: 2 givenname: Bingyang orcidid: 0009-0004-3268-6278 surname: Zhang fullname: Zhang, Bingyang organization: College of Computer Science, Zhejiang University of Technology, Hangzhou, China – sequence: 3 givenname: Kaituo orcidid: 0009-0004-6976-1884 surname: Zhang fullname: Zhang, Kaituo organization: College of Computer Science, Zhejiang University of Technology, Hangzhou, China – sequence: 4 givenname: Hua orcidid: 0000-0002-4078-3527 surname: Gao fullname: Gao, Hua email: ghua@zjut.edu.cn organization: College of Computer Science, Zhejiang University of Technology, Hangzhou, China – sequence: 5 givenname: Rongchun orcidid: 0009-0008-4747-2247 surname: Wan fullname: Wan, Rongchun organization: Zhejiang HOUDAR Intelligent Technology Company Ltd., Hangzhou, China |
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| Cites_doi | 10.48550/arXiv.1312.6114 10.5555/3045118.3045167 10.1109/CVPRW56347.2022.00080 10.1109/access.2018.2816564 10.1023/B:MACH.0000008084.60811.49 10.1109/CVPR.2019.00057 10.1145/3292500.3330701 10.1109/cvpr.2018.00684 10.1109/CVPR.2017.76 10.1109/TETCI.2017.2772792 10.1007/978-3-642-21735-7_7 10.1109/CVPR.2019.00982 10.1109/ICCV48922.2021.01333 10.1109/TIM.2021.3098381 10.1109/CVPR52729.2023.00381 10.1109/ICCV.2019.00179 10.1162/neco.1997.9.8.1735 10.1002/int.22582 |
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| SubjectTerms | Accuracy Anomalies Anomaly detection autoencoder Data analysis Data models Datasets deep support vector data description (Deep SVDD) Feature extraction hypersphere collapse Long short term memory LSTM Mathematical models Modules Training Vectors |
| Title | Improved AutoEncoder With LSTM Module and KL Divergence for Anomaly Detection |
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