A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization

TL;DR

The paper introduces GLASS, a unified anomaly synthesis framework using gradient ascent, which enhances unsupervised industrial anomaly detection by covering a broader range of anomalies, especially weak defects, achieving state-of-the-art results.

Contribution

Proposes a novel unified anomaly synthesis strategy combining global and local methods under distribution constraints, improving anomaly coverage and controllability.

Findings

Achieves 99.9% detection AUROC on MVTec AD dataset.

Outperforms existing methods in weak defect detection.

Validated effectiveness in industrial woven fabric defect detection.

Abstract

Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and controllability of anomaly synthesis, particularly for weak defects that are very similar to normal regions. In this paper, we propose Global and Local Anomaly co-Synthesis Strategy (GLASS), a novel unified framework designed to synthesize a broader coverage of anomalies under the manifold and hypersphere distribution constraints of Global Anomaly Synthesis (GAS) at the feature level and Local Anomaly Synthesis (LAS) at the image level. Our method synthesizes near-in-distribution anomalies in a controllable way using Gaussian noise guided by gradient ascent and truncated projection. GLASS achieves state-of-the-art results on the MVTec AD (detection AUROC of 99.9\%), VisA, and MPDD datasets and excels in weak defect detection. The effectiveness and efficiency have been further validated in industrial applications for woven fabric defect detection. The code and dataset are available at: \url{https://github.com/cqylunlun/GLASS}.