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

ArXi:2504.02775v2 Announce Type: replace-cross 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 on tail class samples, and vice versa. To mitigate the issue, we handle the tail class and noise samples independently.