CVAD - An unsupervised image anomaly detector
| dc.contributor.author | Guo, Xiaoyuan | |
| dc.contributor.author | Gichoya, Judy Wawira | |
| dc.contributor.author | Purkayastha, Saptarshi | |
| dc.contributor.author | Banerjee, Imon | |
| dc.contributor.department | BioHealth Informatics, School of Informatics and Computing | en_US |
| dc.date.accessioned | 2022-10-05T19:10:50Z | |
| dc.date.available | 2022-10-05T19:10:50Z | |
| dc.date.issued | 2022-02 | |
| dc.description.abstract | 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. | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.identifier.citation | Guo, 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.2021.100195 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1805/30201 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.isversionof | 10.1016/j.simpa.2021.100195 | en_US |
| dc.relation.journal | Software Impacts | en_US |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.source | Publisher | en_US |
| dc.subject | anomaly detection | en_US |
| dc.subject | OOD detection | en_US |
| dc.subject | variational autoencoder | en_US |
| dc.title | CVAD - An unsupervised image anomaly detector | en_US |
| dc.type | Article | en_US |