Unsupervised anomaly localization, which plays a critical role in industrial manufacturing, aims to identify anomalous regions that deviate from normal sample patterns. Most recent methods perform feature matching or reconstruction for the target sample with pre-trained deep neural networks. However, they still struggle to address challenging anomalies because the deep embeddings stored in the memory bank can be less powerful and informative. More specifically, prior methods often overly rely on the finite resources stored in the memory bank, which leads to low robustness to unseen targets. In this paper, we propose a novel subspace-guided feature reconstruction framework to pursue adaptive feature approximation for anomaly localization. It first learns to construct low-dimensional subspaces from the given nominal samples, and then learns to reconstruct the given deep target embedding by linearly combining the subspace basis vectors using the self-expressive model. Our core is that, despite the limited resources in the memory bank, the out-of-bank features can be alternatively ``mimicked'' under the self-expressive mechanism to adaptively model the target. Eventually, the poorly reconstructed feature dimensions indicate anomalies for localization. Moreover, we propose a sampling method that leverages the sparsity of subspaces and allows the feature reconstruction to depend only on a small resource subset, which contributes to less memory overhead. Extensive experiments on three industrial benchmark datasets demonstrate that our approach generally achieves state-of-the-art anomaly localization performance.

翻译：无监督异常定位在工业制造中具有关键作用，旨在识别偏离正常样本模式的异常区域。近期多数方法利用预训练深度神经网络对目标样本进行特征匹配或重构。然而，由于存储库中保存的深度嵌入可能不够强大和信息丰富，这些方法仍难以应对具有挑战性的异常。具体而言，先前方法往往过度依赖存储库中有限的资源，导致对未见目标的鲁棒性较低。本文提出一种新颖的子空间引导特征重构框架，以实现自适应特征逼近用于异常定位。该方法首先学习从给定名义样本中构建低维子空间，随后通过自表达模型线性组合子空间基向量，学习重构给定的深度目标嵌入。其核心在于：尽管存储库资源有限，但可通过自表达机制对库外特征进行替代性“模拟”，以自适应地对目标建模。最终，重构质量较差的特征维度指示异常区域用于定位。此外，我们提出一种利用子空间稀疏性的采样方法，使得特征重构仅依赖少量资源子集，从而降低内存开销。在三个工业基准数据集上的大量实验表明，本方法普遍达到最先进的异常定位性能。