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

Qisen Cheng; Shuhui Qu; Janghwan Lee

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arXiv · 2501.09187

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

Qisen Cheng·Shuhui Qu·Janghwan Lee

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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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BibTeX RIS

Qisen Cheng, Shuhui Qu, Janghwan Lee. 2025-01-15. Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detection. https://arxiv.org/abs/2501.09187

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