Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection
This addresses the problem of detecting subtle defects in automated industrial inspection, offering a novel method that improves accuracy for this domain-specific application.
The paper tackled structural anomaly detection in industrial inspection by introducing a self-supervised autoencoder with a corruption model that uses structured perturbations and Gaussian noise as a Tikhonov regularizer, achieving state-of-the-art results on the MVTec AD benchmark with I/P-AUROC scores of 99.9/99.4.
Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns. As collecting representative examples of all possible anomalies is infeasible, we tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs. To this end, we introduce a corruption model that injects artificial disruptions into training images to mimic structural defects. While reminiscent of denoising autoencoders, our approach differs in two key aspects. First, instead of unstructured i.i.d.\ noise, we apply structured, spatially coherent perturbations that make the task a hybrid of segmentation and inpainting. Second, and counterintuitively, we add and preserve Gaussian noise on top of the occlusions, which acts as a Tikhonov regularizer anchoring the Jacobian of the reconstruction function toward identity. This identity-anchored regularization stabilizes reconstruction and further improves both detection and segmentation accuracy. On the MVTec AD benchmark, our method achieves state-of-the-art results (I/P-AUROC: 99.9/99.4), supporting our theoretical framework and demonstrating its practical relevance for automatic inspection.