Adaptive aggregation-distillation autoencoder for unsupervised anomaly detection

Jiaqi Zhu; Fang Deng; Jiachen Zhao; Jie Chen

doi:10.1016/j.patcog.2022.108897

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Adaptive aggregation-distillation autoencoder for unsupervised anomaly detection

Jiaqi Zhu Fang Deng Jiachen Zhao Jie Chen

Year: 2022 Journal: Pattern Recognition Vol: 131 Pages: 108897-108897 Publisher: Elsevier BV

DOI: 10.1016/j.patcog.2022.108897

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Keywords:

Computer science Autoencoder Anomaly detection Artificial intelligence Pattern recognition (psychology) Distillation Process (computing) Generalization Landmark Data mining Machine learning Mathematics Artificial neural network Chemistry

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Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Data-Driven Disease Surveillance

Health Sciences → Medicine → Epidemiology

Adaptive aggregation-distillation autoencoder for unsupervised anomaly detection

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