  In the field of multi-class anomaly detection, reconstruction-based methods
derived from single-class anomaly detection face the well-known challenge of
"learning shortcuts", wherein the model fails to learn the patterns of normal
samples as it should, opting instead for shortcuts such as identity mapping or
artificial noise elimination. Consequently, the model becomes unable to
reconstruct genuine anomalies as normal instances, resulting in a failure of
anomaly detection. To counter this issue, we present a novel unified feature
reconstruction-based anomaly detection framework termed RLR (Reconstruct
features from a Learnable Reference representation). Unlike previous methods,
RLR utilizes learnable reference representations to compel the model to learn
normal feature patterns explicitly, thereby prevents the model from succumbing
to the "learning shortcuts" issue. Additionally, RLR incorporates locality
constraints into the learnable reference to facilitate more effective normal
pattern capture and utilizes a masked learnable key attention mechanism to
enhance robustness. Evaluation of RLR on the 15-category MVTec-AD dataset and
the 12-category VisA dataset shows superior performance compared to
state-of-the-art methods under the unified setting. The code of RLR will be
publicly available.
