Bibliographic description:
Yelisieiev, Yurii. Anomaly detection with sinusoidal representation network / Yurii Yelisieiev; Supervisor: PhD Taras Firman; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2021. – 48 p.: ill.
Abstract:
Anomaly detection in images helps to identify abnormal or unusual patterns in images
relative to normal data. This issue is crucial in various domains, such as detecting
abnormal areas in medical imaging, surface inspection, or photo editing. Many
of the proposed methods require additional markup on the images. One way to
solve this problem is image reconstruction. This method does not require additional
markup on training data and trains on normal images. In this study, we present an
approach that solves the Image Reconstruction problem by exploiting the Sinusoidal
Representation Network (SIREN). SIREN is capable of modeling complex signals
with great detail in small regions. We experimentally combined different loss functions
for our architecture to improve the visual perception of the image. This study
will also describe the different approaches we have tried in image reconstruction
and our method’s evolution.
