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产品缺陷（如错误零件、组件错位及损伤）的早期识别至关重要。由于缺陷观测样本稀少且类型未知，异常检测被认为是机器学习领域的挑战性任务。为克服这一困难，近期方法利用从自然图像数据集中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方案（尤其针对工业数据集）。本文引入ReConPatch方法，通过训练预训练模型提取补丁特征的线性调制，构建具有判别性的异常检测特征。ReConPatch采用对比表征学习，以生成面向目标且易分离的表征方式收集与分布特征。针对对比学习中标注对缺失的问题，我们利用数据表征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。该方法在广泛使用的挑战性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数据集上取得了最先进性能（95.8%）。