  There have been significant advancements in anomaly detection in an
unsupervised manner, where only normal images are available for training.
Several recent methods aim to detect anomalies based on a memory, comparing or
reconstructing the input with directly stored normal features (or trained
features with normal images). However, such memory-based approaches operate on
a discrete feature space implemented by the nearest neighbor or attention
mechanism, suffering from poor generalization or an identity shortcut issue
outputting the same as input, respectively. Furthermore, the majority of
existing methods are designed to detect single-class anomalies, resulting in
unsatisfactory performance when presented with multiple classes of objects. To
tackle all of the above challenges, we propose CRAD, a novel anomaly detection
method for representing normal features within a "continuous" memory, enabled
by transforming spatial features into coordinates and mapping them to
continuous grids. Furthermore, we carefully design the grids tailored for
anomaly detection, representing both local and global normal features and
fusing them effectively. Our extensive experiments demonstrate that CRAD
successfully generalizes the normal features and mitigates the identity
shortcut, furthermore, CRAD effectively handles diverse classes in a single
model thanks to the high-granularity continuous representation. In an
evaluation using the MVTec AD dataset, CRAD significantly outperforms the
previous state-of-the-art method by reducing 65.0% of the error for multi-class
unified anomaly detection. The project page is available at
https://tae-mo.github.io/crad/.
