Conference
Robust Anomaly Detection under Data Contamination for Pixel-Level Defective Region Segmentation
This work proposes an unsupervised robust anomaly detection framework under data contamination for pixel-level defective region segmentation, built on an autoencoder structure with a memory module and holds two mild assumptions: across-sample low-rank, and sparse anomalies.
Yi-Feng Wang, Jian-Jun Shi, Chuck Zhang et al.
· IISE Annual Conference &... · 0 citations