  Conventional unsupervised anomaly detection (UAD) methods build separate
models for each object category. Recent studies have proposed to train a
unified model for multiple classes, namely model-unified UAD. However, such
methods still implement the unified model separately on each class during
inference with respective anomaly decision thresholds, which hinders their
application when the image categories are entirely unavailable. In this work,
we present a simple yet powerful method to address multi-class anomaly
detection without any class information, namely \textit{absolute-unified} UAD.
We target the crux of prior works in this challenging setting: different
objects have mismatched anomaly score distributions. We propose Class-Agnostic
Distribution Alignment (CADA) to align the mismatched score distribution of
each implicit class without knowing class information, which enables unified
anomaly detection for all classes and samples. The essence of CADA is to
predict each class's score distribution of normal samples given any image,
normal or anomalous, of this class. As a general component, CADA can activate
the potential of nearly all UAD methods under absolute-unified setting. Our
approach is extensively evaluated under the proposed setting on two popular UAD
benchmark datasets, MVTec AD and VisA, where we exceed previous
state-of-the-art by a large margin.
