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

Abstract--Unsupervised visual defect detection is essential across various industrial applications. Typically, this involves learning a representation space that captures only the features of normal data, and subsequently identifying defects by measuring deviations from this norm. However, balancing the expressiveness and compactness of this space is challenging. The space must be comprehensive enough to encapsulate all regular patterns of normal data, yet without becoming overly expressive, which leads to wasted computational and storage resources and may cause mode collapse--blurring the distinction between normal and defect data embeddings and impairing detection accuracy. To overcome these issues, we introduce a novel approach using an extended VQ-VAE framework optimized for unsupervised defect detection.