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

Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly de-tection and localization. Despite this progress, these meth-ods still face challenges in synthesizing realistic and di-verse 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 inno-vations: First, we propose Strength-controllable Diffusion Anomaly Synthesis (SDAS), a diffusion process-based syn-thesis strategy capable of generating samples with varying anomaly strengths that mimic the distribution of real anomalous samples. Second, we develop Anomaly-aware Features Selection (A FS), a method for selecting repre-sentative and discriminative pre-trained feature subsets to improve anomaly detection performance while controlling computational costs. Third, we introduce Reconstruction Residuals Selection (RRS), a strategy that adaptively selects discriminative residuals for comprehensive identification of anomalous regions across multiple levels of granularity. We assess RealNet onfour benchmark datasets, and our results demonstrate significant improvements in both Image AU-Rae and Pixel AUROC compared to the current state-of-the-art methods. The code, data, and models are available at https://github.com/cnulab/RealNet.