mlsanityData to 14 Oct 2026

SPACE: SPAtial-aware Consistency rEgularization for anomaly detection in Industrial applications

arXiv 2411.05822 ↗First posted 5 Nov 2024cs.CVcited by 2

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Abstract (arXiv)

In this paper, we propose SPACE, a novel anomaly detection methodology that integrates a Feature Encoder (FE) into the structure of the Student-Teacher method. The proposed method has two key elements: Spatial Consistency regularization Loss (SCL) and Feature converter Module (FM). SCL prevents overfitting in student models by avoiding excessive imitation of the teacher model. Simultaneously, it facilitates the expansion of normal data features by steering clear of abnormal areas generated through data augmentation. This dual functionality ensures a robust boundary between normal and abnormal data. The FM prevents the learning of ambiguous information from the FE. This protects the learned features and enables more effective detection of structural and logical anomalies. Through these elements, SPACE is available to minimize the influence of the FE while integrating various data augmentations.In this study, we evaluated the proposed method on the MVTec LOCO, MVTec AD, and VisA datasets. Experimental results, through qualitative evaluation, demonstrate the superiority of detection and efficiency of each module compared to state-of-the-art methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 36 of its 62 references that are in the map. First posted on arXiv 5 Nov 2024.

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36 of 62 references.

References · 36

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24 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bankcited by 0
  2. 2026Continuity-Driven Representation Learning for Industrial Defect Detectioncited by 0