SoftPatch: Unsupervised Anomaly Detection with Noisy Data

arXiv:2403.14233v1 Announce Type: cross
Abstract: Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the …

abstract academic algorithms anomaly anomaly detection application arxiv cs.ai cs.cv cs.lg data datasets detection experimental paper performance practical training training data type unsupervised world