Deep Orthogonal Hypersphere Compression for Anomaly Detection

arXiv:2302.06430v2 Announce Type: replace
Abstract: Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces. In this paper, we first propose a novel deep anomaly detection model that improves the original hypersphere learning through an orthogonal projection layer, which ensures that the training data distribution is consistent with the hypersphere hypothesis, thereby …

abstract anomaly anomaly detection arxiv compact compression cs.ai cs.lg data decision detection detection methods however novel paper practice spaces type