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.

本研究提出了一种名为 ReConPatch 的方法，通过训练与预训练模型相关的线性调制来构建判别性特征，采用对比表示学习来以目标为导向地收集和分布特征，实现了无需大量输入增强即可实现强健的异常检测性能。