Fourier-Convolutional PaDiM for Anomaly Detection

Abstract

Anomaly detection aims to detect unusual patterns and samples in a training distribution. In this domain, many researchers have paid attention to anomaly detection models using ImageNet-pretrained weights. Among them, PaDiM is a promising approach that detects anomalies based on the feature distribution. While such approaches have achieved significant results, they tend to overlook global information due to the texture bias caused by ImageNet-pretrained convolutional models. Therefore, in this paper, we propose incorporating Fast Fourier Convolution, which can extract global information in the frequency domain, into PaDiM. This proposed model is named Fourier-Convolutional PaDiM (FC-PaDiM). Our FC-PaDiM is able to extract global features from frequency space and local features from feature space for more accurate anomaly detection. In our experiments, we demonstrated that our proposed FC-PaDiM allowed for extracting local and global features compared to PaDiM. Moreover, our additional analysis revealed the robustness of perturbations in frequency bands in the MVTecAD dataset.