Combines visual prompting, dual-teacher supervision, and diffusion augmentation on an MMR backbone to gain 3.5 percentage points on the AeBAD anomaly detection dataset.
Han Xiao, Kashif Rasul, and Roland V ollgraf
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Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
Combines visual prompting, dual-teacher supervision, and diffusion augmentation on an MMR backbone to gain 3.5 percentage points on the AeBAD anomaly detection dataset.