Unsupervised anomaly localization, which plays a critical role in industrial manufacturing, is to identify anomalous regions that deviate from patterns established exclusively from nominal samples. Recent mainstream methods focus on approximating the target feature distribution by leveraging embeddings from ImageNet models. However, a common issue in many anomaly localization methods is the lack of adaptability of the feature approximations to specific targets. Consequently, their ability to effectively identify anomalous regions relies significantly on the data coverage provided by the finite resources in a memory bank. In this paper, we propose a novel subspace-aware feature reconstruction framework for anomaly localization. To achieve adaptive feature approximation, our proposed method involves the reconstruction of the feature representation through the self-expressive model designed to learn low-dimensional subspaces. Importantly, the sparsity of the subspace representation contributes to covering feature patterns from the same subspace with fewer resources, leading to a reduction in the memory bank. Extensive experiments across three industrial benchmark datasets demonstrate that our approach achieves competitive anomaly localization performance compared to state-of-the-art methods by adaptively reconstructing target features with a small number of samples.

翻译：无监督异常定位在工业制造中扮演关键角色，其目标是识别偏离仅由正常样本构建模式的异常区域。近期主流方法通过利用ImageNet模型提取的嵌入特征来逼近目标特征分布。然而，许多异常定位方法普遍存在特征逼近对特定目标缺乏自适应性的问题。因此，其有效识别异常区域的能力高度依赖于记忆库中有限资源的数据覆盖范围。本文提出一种新颖的基于子空间感知的特征重构框架用于异常定位。为实现自适应特征逼近，所提方法通过构建用于学习低维子空间的自表达模型来重构特征表示。重要的是，子空间表示的稀疏性有助于用更少的资源覆盖同一子空间中的特征模式，从而减少记忆库规模。在三个工业基准数据集上的大量实验表明，本方法通过用少量样本自适应重构目标特征，实现了与现有最先进方法相比具有竞争力的异常定位性能。