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.

我们从频域角度提出了 Dual-Path Frequency Discriminators (DFD) 网络，用于解决少样本异常检测中的问题，并通过鉴别分类模块在特征空间中检测和定位图像级别和特征级别的异常，实验证明我们的方法超过了现有的最先进方法。