Symmetry and Asymmetry in Adaptive Selection and Fuzzy Fusion for Industrial Anomaly Detection: The MSPNet Framework

Unsupervised industrial anomaly detection learns from normal images and identifies defects unseen during training. In reconstruction-based detectors, the features used to represent normality and the residuals used to localize defects vary across images, yet these signals are commonly selected with fixed category-level rules. We propose MSPNet, a Mirrored Selection Policy Network that learns these choices from validation performance. Its feature selection policy chooses channels that characterize normal appearance, and its residual selection policy chooses residuals for localization. The policies share a reward based on image-level and pixel-level AUROC while acting on complementary stages of the reconstruction pipeline, so the pair is symmetric in structure while the stages it acts on are not. Their scores are combined by SA-Fuzzy, a compact Takagi–Sugeno layer whose inputs are symmetric under exchange of the two branches, and whose membership functions are symmetric in branch agreement but asymmetric in anomaly level, so that increasing and decreasing anomaly signals receive different responses. On MVTec-AD, VisA, MPDD and BTAD, MSPNet obtains Image-AUROC/Pixel-AUROC values of 99.6/99.1, 98.2/99.0, 97.1/98.6 and 96.6/98.1, respectively. The learned policies are most useful for images whose selections depart from category averages, and SA-Fuzzy improves over a raw-score combiner on the images where the two branches disagree. On the full set, the two combiners are indistinguishable. Most of that margin comes from the exchange-invariant parameterization of the layer inputs. The present resolution does not separate fuzzy inference itself from a parameter-matched alternative. The learnable asymmetric memberships do separate from their symmetric counterpart on the same subset. These results show that input-adaptive selection can improve reconstruction-based anomaly detection across changing image appearances.