Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behind state-of-the-art class-separated models. Our research aims to bridge this substantial performance gap. In this paper, we present Dinomaly, a minimalist reconstruction-based anomaly detection framework that harnesses pure Transformer architectures without relying on complex designs, additional modules, or specialized tricks. Given this powerful framework consisting of only Attentions and MLPs, we found four simple components that are…

• NN
  National Natural Science Foundation of China
• TU
  Tsinghua University
• NK
  National Key Research and Development Program of China
• HE
  HORIZON EUROPE Health