ReConPatch：用于工业异常检测的对比块表示学习
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
AI总结：
本文提出ReConPatch方法，通过对比表示学习训练预训练块特征的线性调制，利用成对和上下文相似性作为伪标签，在MVTec AD和BTAD数据集上实现了最先进的工业异常检测性能。
摘要：
异常检测对于工业制造中产品缺陷（如错误零件、错位组件和损坏）的高级识别至关重要。由于缺陷观察罕见且类型未知，异常检测在机器学习中被认为具有挑战性。为了克服这一困难，最近的方法利用从自然图像数据集预训练的通用视觉表示，并提取相关特征。然而，现有方法在预训练特征与目标数据之间仍存在差异，或者需要精心设计的输入增强，特别是对于工业数据集。在本文中，我们引入了ReConPatch，它通过训练从预训练模型提取的块特征的线性调制来构建用于异常检测的判别性特征。ReConPatch采用对比表示学习，以产生目标导向且易于分离的表示的方式来收集和分布特征。为了解决对比学习缺乏标记对的问题，我们利用数据表示之间的两种相似性度量（成对相似性和上下文相似性）作为伪标签。我们的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%）。此外，我们在BTAD数据集上实现了最先进的异常检测性能（95.8%）。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
