Few-shot anomaly detection (FSAD) is essential in industrial manufacturing. However, existing FSAD methods struggle to effectively leverage a limited number of normal samples, and they may fail to detect and locate inconspicuous anomalies in the spatial domain. We further discover that these subtle anomalies would be more noticeable in the frequency domain. In this paper, we propose a Dual-Path Frequency Discriminators (DFD) network from a frequency perspective to tackle these issues. Specifically, we generate anomalies at both image-level and feature-level. Differential frequency components are extracted by the multi-frequency information construction module and supplied into the fine-grained feature construction module to provide adapted features. We consider anomaly detection as a discriminative classification problem, wherefore the dual-path feature discrimination module is employed to detect and locate the image-level and feature-level anomalies in the feature space. The discriminators aim to learn a joint representation of anomalous features and normal features in the latent space. Extensive experiments conducted on MVTec AD and VisA benchmarks demonstrate that our DFD surpasses current state-of-the-art methods. Source code will be available.

翻译：少样本异常检测在工业制造中至关重要。然而，现有少样本异常检测方法难以有效利用有限数量的正常样本，并且可能在空间域中无法检测和定位到不明显的异常。我们进一步发现，这些细微异常在频率域中会更加显著。本文从频率视角出发，提出了一种双路径频率判别器网络来解决这些问题。具体而言，我们在图像级和特征级上生成异常。通过多频率信息构建模块提取差分频率成分，并将其输入细粒度特征构建模块以提供自适应特征。我们将异常检测视为判别分类问题，从而采用双路径特征判别模块在特征空间中检测和定位图像级与特征级异常。该判别器旨在学习潜在空间中异常特征与正常特征的联合表示。在MVTec AD和VisA基准上开展的大量实验表明，我们的DFD方法超越了当前最先进的方法。源代码将公开提供。