  In the domain of anomaly detection, methods often excel in either high-level
semantic or low-level industrial benchmarks, rarely achieving cross-domain
proficiency. Semantic anomalies are novelties that differ in meaning from the
training set, like unseen objects in self-driving cars. In contrast, industrial
anomalies are subtle defects that preserve semantic meaning, such as cracks in
airplane components. In this paper, we present GeneralAD, an anomaly detection
framework designed to operate in semantic, near-distribution, and industrial
settings with minimal per-task adjustments. In our approach, we capitalize on
the inherent design of Vision Transformers, which are trained on image patches,
thereby ensuring that the last hidden states retain a patch-based structure. We
propose a novel self-supervised anomaly generation module that employs
straightforward operations like noise addition and shuffling to patch features
to construct pseudo-abnormal samples. These features are fed to an
attention-based discriminator, which is trained to score every patch in the
image. With this, our method can both accurately identify anomalies at the
image level and also generate interpretable anomaly maps. We extensively
evaluated our approach on ten datasets, achieving state-of-the-art results in
six and on-par performance in the remaining for both localization and detection
tasks.
