Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories. (arXiv:2308.05011v1 [cs.LG])

With the increasing volume of astronomical data generated by modern survey
telescopes, automated pipelines and machine learning techniques have become
crucial for analyzing and extracting knowledge from these datasets. Anomaly
detection, i.e. the task of identifying irregular or unexpected patterns in the
data, is a complex challenge in astronomy. In this paper, we propose
Multi-Class Deep Support Vector Data Description (MCDSVDD), an extension of the
state-of-the-art anomaly detection algorithm One-Class Deep SVDD, specifically
designed to handle different inlier categories with …

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