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.

翻译：异常检测对于工业制造中零件错误、组件错位及损伤等产品缺陷的早期识别至关重要。由于缺陷观测稀少且类型未知，异常检测被视为机器学习领域的挑战性问题。为克服这一困难，近期方法利用自然图像数据集预训练的通用视觉表征，并提取相关特征。然而，现有方法仍存在预训练特征与目标数据之间的差异，或需要精心设计的输入增强，尤其针对工业数据集。本文提出ReConPatch方法，通过训练预训练模型提取的补丁特征的线性调制，构建用于异常检测的判别性特征。ReConPatch采用对比表示学习，以产生面向目标且易于分离的特征分布形式来收集与分布特征。为解决对比学习中标注样本对缺失的问题，我们利用数据表示之间的两种相似度度量——成对相似度与上下文相似度——作为伪标签。本方法在广泛使用且具有挑战性的MVTec AD数据集上实现了最先进的异常检测性能（99.72%），同时在BTAD数据集上也取得了最优异常检测结果（95.8%）。