Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection
This work addresses the practical need for unsupervised anomaly detection without costly extra training data, offering a significant improvement over existing methods.
The paper tackles the problem of poor performance in reconstruction-based unsupervised anomaly detection by proposing an Omni-frequency Channel-selection Reconstruction (OCR-GAN) network, achieving a state-of-the-art 98.3 detection AUC on the MVTec AD dataset, which surpasses the baseline by +38.1 and the current SOTA by +0.3.
Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance. However, the latter requires no costly extra training samples for the unsupervised training that is more practical, so this paper focuses on improving this kind of method and proposes a novel Omni-frequency Channel-selection Reconstruction (OCR-GAN) network to handle anomaly detection task in a perspective of frequency. Concretely, we propose a Frequency Decoupling (FD) module to decouple the input image into different frequency components and model the reconstruction process as a combination of parallel omni-frequency image restorations, as we observe a significant difference in the frequency distribution of normal and abnormal images. Given the correlation among multiple frequencies, we further propose a Channel Selection (CS) module that performs frequency interaction among different encoders by adaptively selecting different channels. Abundant experiments demonstrate the effectiveness and superiority of our approach over different kinds of methods, e.g., achieving a new state-of-the-art 98.3 detection AUC on the MVTec AD dataset without extra training data that markedly surpasses the reconstruction-based baseline by +38.1 and the current SOTA method by +0.3. Source code is available at https://github.com/zhangzjn/OCR-GAN.