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

Ximiao Zhang, Min Xu, Xiuzhuang Zhou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 16699-16708

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 (SDAS) a diffusion process-based synthesis strategy capable of generating samples with varying anomaly strengths that mimic the distribution of real anomalous samples. Second we develop Anomaly-aware Features Selection (AFS) a method for selecting representative 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 on four benchmark datasets and our results demonstrate significant improvements in both Image AUROC 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.