OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection 事件

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection arXiv:2507.21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available. Most state-of-the-art methods fall into two categories: reconstruction-based approaches, which often reconstruct anomalies too well, and decoupled representation learning with density