  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 pre-trained 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
of patch features extracted from the 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 between data representations, pairwise and contextual
similarities, as pseudo-labels. Our method achieves the state-of-the-art
anomaly detection performance (99.72%) for the widely used and challenging
MVTec AD dataset. Additionally, we achieved a state-of-the-art anomaly
detection performance (95.8%) for the BTAD dataset.
