Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with
Dual Conditioning
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with
Dual Conditioning
Anomaly detection, the technique of identifying abnormal samples using only normal samples, has attracted widespread interest in industry. Existing one-model-per-category methods often struggle with limited generalization capabilities due to their focus on a single category, and can fail when encountering variations in product. Recent feature reconstruction methods, as representatives in …