Corpus ID: 57825713

Deep Learning for Anomaly Detection: A Survey

@inproceedings{Sydney2019DeepLF,
  title={Deep Learning for Anomaly Detection: A Survey},
  author={Raghavendra Chalapathy University of Sydney and Capital Markets Cooperative Research Centre and Sanjay Chawla Qatar Computing Research Institute and Hbku},
  year={2019}
}
Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art deep anomaly detection research techniques into… Expand
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