The paper introduces a new and practical anomaly detection setting, i.e., absolute-unified multi-class unsupervised anomaly detection (UAD), where a unified model is trained and evaluated on a mixture of multiple categories without class labels.
The authors identify the key issue in this setting - different object classes have mismatched anomaly score distributions, which hinders the application of prior methods that rely on class information during inference. To address this, the authors propose CADA, which predicts the anomaly score distribution of normal samples for each implicit class and normalizes the anomaly map accordingly. This allows the normal regions of each class to conform to the same distribution, enabling unified anomaly detection.
CADA is extensively evaluated on two popular industrial defect detection benchmarks, MVTec AD and VisA, where it significantly boosts the performance of conventional UAD methods under the absolute-unified setting. The authors achieve state-of-the-art image-level AUROC of 98.6% on MVTec AD, exceeding the previous best by a large margin.
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