Abstract: Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction ...

Abstract: As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In ...

With the rise of generative models, there is a growing interest in unifying all tasks within a generative framework. Anomaly detection methods also fall into this scope and utilize diffusion models to ...