Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection

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 reconstruction-based method and proposes a novel O mni-frequency C hannel-selection R econstruction (OCR-GAN) network to handle sensory 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…

• NN
  National Natural Science Foundation of China

  Award: U21A20484
• KR
  Key Research and Development Program of Zhejiang Province

  Award: 2022C01011