CVAD - An unsupervised image anomaly detector
Xiaoyuan Guo, Judy Wawira Gichoya, Saptarshi Purkayastha, Imon Banerjee · Software Impacts · 2021
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 C ascade V ariational autoencoder-based A nomaly D etector , 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.