Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection

18 Feb 2020Haoyi FanFengbin ZhangRuidong WangLiang XiZuoyong Li

Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications. However, most existing methods neglect the complex correlation among data samples, which is important for capturing normal patterns from which the abnormal ones deviate... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.