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.

我们提出了一种新颖的子空间感知特征重构框架，用于异常定位，通过学习低维子空间的自表达模型实现自适应特征逼近，从而减少内存需求并实现与最先进方法相媲美的异常定位性能。