ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

26 May 2023  ·  Jeeho Hyun, Sangyun Kim, Giyoung Jeon, Seung Hwan Kim, Kyunghoon Bae, Byung Jun Kang ·

Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the common visual representations from natural image datasets and distill the relevant features. However, existing approaches still have the discrepancy between the pre-trained feature and the target data, or require the input augmentation which should be carefully designed particularly for the industrial dataset. In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation attached to a pre-trained model. ReConPatch employs contrastive representation learning to collect and distribute features in a way that produces a target-oriented and easily separable representation. To address the absence of labeled pairs for the contrastive learning, we utilize two similarity measures, pairwise and contextual similarities, between data representations as a pseudo-label. Unlike previous work, ReConPatch achieves robust anomaly detection performance without extensive input augmentation. Our method achieves the state-of-the-art anomaly detection performance (99.72%) for the widely used and challenging MVTec AD dataset.

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Datasets


Results from the Paper


Ranked #3 on Anomaly Detection on MVTec AD (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Anomaly Detection MVTec AD ReConPatch Ensemble Detection AUROC 99.72 # 3
Segmentation AUROC 98.67 # 12
Anomaly Detection MVTec AD ReConPatch WRN-50 Detection AUROC 99.56 # 10
Segmentation AUROC 98.18 # 29
Anomaly Detection MVTec AD ReConPatch WRN-101 Detection AUROC 99.62 # 8
Segmentation AUROC 98.53 # 14

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