Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detection

• URL: http://arxiv.org/abs/2501.09187v1
• Date: Wed, 15 Jan 2025 22:26:26 GMT
• Title: Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detection
• Authors: Qisen Cheng, Shuhui Qu, Janghwan Lee,
• Abstract summary: Unsupervised visual defect detection is critical in industrial applications.<n>We propose a novel approach using an enhanced VQ-VAE framework optimized for unsupervised defect detection.
• Score: 4.081433571732692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised visual defect detection is critical in industrial applications, requiring a representation space that captures normal data features while detecting deviations. Achieving a balance between expressiveness and compactness is challenging; an overly expressive space risks inefficiency and mode collapse, impairing detection accuracy. We propose a novel approach using an enhanced VQ-VAE framework optimized for unsupervised defect detection. Our model introduces a patch-aware dynamic code assignment scheme, enabling context-sensitive code allocation to optimize spatial representation. This strategy enhances normal-defect distinction and improves detection accuracy during inference. Experiments on MVTecAD, BTAD, and MTSD datasets show our method achieves state-of-the-art performance.

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