CVAD - An unsupervised image anomaly detector[Formula presented]

Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha, Imon Banerjee

Research output: Contribution to journalArticlepeer-review


Detecting out-of-distribution samples for image applications plays an important role in safeguarding the reliability of machine learning model deployment. In this article, we developed a software tool to support our OOD detector CVAD - a self-supervised Cascade Variational autoencoder-based Anomaly Detector, which can be easily applied to various image applications without any assumptions. The corresponding open-source software is published for better public research and tool usage.

Original languageEnglish (US)
Article number100195
JournalSoftware Impacts
StatePublished - Feb 2022


  • Anomaly detection
  • OOD detection
  • Variational autoencoder

ASJC Scopus subject areas

  • Software


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