Print Email Facebook Twitter Exploring normalizing flow for anomaly detection Title Exploring normalizing flow for anomaly detection Author Pathak, Chinmay (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor van Gemert, Jan (mentor) Reinders, Marcel (graduation committee) van Genderen, Arjan (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering Date 2019-08-29 Abstract Anomaly detection is a task of interest in many domains. Typical way of tackling this problem is using an unsupervised way. Recently, deep neural network based density estimators such as Normalizing flows have seen a huge interest. The ability of these models to do the exact latent-variable inference and exact log-likelihood calculation with invertible architecture makes them interesting for the task of anomaly detection. In this work we explore the such normalizing flow-based model approach for anomaly detection in the novel BoroscopeV1 dataset which contains videos of the actual industry boroscope video material and has large noise. We verify the correctness of the models on a toy dataset. We found that the black pixels and high frequency in the image affect the model likelihood adversely. The experimental evidence shows that the normalizing flow-based approach can be used for the task of anomaly detection Subject Anomaly DetectionOutlier detectionAutoencoderGenerative Algorithmsunsupervised learningone-class classificationGLOWNormalizing flows To reference this document use: http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852 Part of collection Student theses Document type master thesis Rights © 2019 Chinmay Pathak Files PDF Complete_Report_Chinmay_Pathak.pdf 5.08 MB Close viewer /islandora/object/uuid:f5588df3-626d-42cc-8059-32e5bb16d852/datastream/OBJ/view