RealNet: A Feature Selection Network with Realistic Synthetic Anomaly
  for Anomaly Detection
Ximiao Zhang Min Xu Xiuzhuang Zhou
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-o-the-art
methods. The code, data, and models are available at
https://github.com/cnulab/RealNet.