ODD: ONE-CLASS ANOMALY DETECTION VIA THE DIFFUSION MODEL

Anomaly detection identifies instances that deviate the distribution of the normal class. Recently, the diffusion models have shown great promise. Our research revealed that by training the diffusion model solely on normal data, it is able to transform both normal and anomalous samples into normal images. Employing this discovery, we propose ODD (One-Class Anomaly Detection via the Diffusion model), which consists of: a diffusion model to convert both normal and anomalous data into normal data, and a similarity network enhanced with outlier exposure to measure the semantic distance between the input and output of the diffusion model. If the score is low, the input is considered as an anomaly instance. The ODD is evaluated on a variety of datasets. Both qualitative and quantitative results demonstrate that our method outperforms existing state-of-the-art techniques.