Guo, XiaoyuanGichoya, Judy WawiraPurkayastha, SaptarshiBanerjee, Imon2022-10-052022-10-052022-02Guo, X., Gichoya, J. W., Purkayastha, S., & Banerjee, I. (2022). CVAD-An unsupervised image anomaly detector. Software Impacts, 11, 100195. https://doi.org/10.1016/j.simpa.2021.100195https://hdl.handle.net/1805/30201Detecting 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.enAttribution 4.0 Internationalanomaly detectionOOD detectionvariational autoencoderArticle