Deep Anomaly Detection with Deviation Networks

19 Nov 2019Guansong PangChunhua ShenAnton van den Hengel

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Anomaly Detection Census DevNet AUC 0.828 # 1
Average Precision 0.321 # 1
Fraud Detection Kaggle-Credit Card Fraud Dataset DevNet AUC 0.980 # 1
Average Precision 0.690 # 1
Network Intrusion Detection NB15-Backdoor DevNet AUC 0.969 # 1
Average Precision 0.883 # 1
Anomaly Detection Thyroid DevNet AUC 0.783 # 1
Average Precision 0.274 # 1