Regularized Latent Adaptive Framework for Unsupervised Industrial Anomaly Detection via Multi-Scale Generative-Discriminative Learning.

Industrial visual inspection relies heavily on unsupervised anomaly detection due to the scarcity of annotated defect samples. However, existing methods struggle to balance global structural consistency and local defect sensitivity, leading to limited accuracy in practical scenarios. To address this challenge, we propose a unified generative-discriminative framework that combines regularized latent space encoding with multi-scale discriminator-guided supervision. Specifically, a dynamic compactness regularization strategy constrains latent representations of normal samples into a compact manifold to suppress anomaly reconstruction, while a multi-scale discriminator provides hierarchical perceptual constraints to enhance fine-grained anomaly localization across different spatial resolutions. Here we show that the proposed method achieves 98.6% image-level and 98.4% pixel-level AUROC on the MVTec AD benchmark, outperforming state-of-the-art approaches. This framework provides a stable and effective solution for real-world industrial quality inspection.