Is Task-Specific Training Necessary for Anomaly Detection?
Xingwu Zhang, Guanxuan Li, Paul Henderson, Gerardo Aragon-Camarasa et autres
Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder--decoder models to reconstruct anomaly-free features. However, we argue that such task-specific training is costly under distribution shifts, and that reconstruction-based residual scoring further faces a fidelity--stability dilemma. Existing training-free alternatives, …
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