RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

arXiv:2403.05897v1 Announce Type: new
Abstract: Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly detection and localization. Despite this progress, these methods still face challenges in synthesizing realistic and diverse anomaly samples, as well as addressing the feature redundancy and pre-training bias of pre-trained feature. In this work, we introduce RealNet, a feature reconstruction network with realistic synthetic anomaly and adaptive feature selection. It is incorporated with three key innovations: First, we propose Strength-controllable Diffusion Anomaly Synthesis …

anomaly anomaly detection arxiv cs.cv detection feature feature selection network synthetic type