TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection 事件

TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection arXiv:2504.02775v2 Announce Type: replace Abstract: We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a model is robust against pixel noise, then its performance deteriorates