  Anomaly detection involves identifying instances within a dataset that
deviate from the norm and occur infrequently. Current benchmarks tend to favor
methods biased towards low diversity in normal data, which does not align with
real-world scenarios. Despite advancements in these benchmarks, contemporary
anomaly detection methods often struggle with out-of-distribution
generalization, particularly in classifying samples with subtle transformations
during testing. These methods typically assume that normal samples during test
time have distributions very similar to those in the training set, while
anomalies are distributed much further away. However, real-world test samples
often exhibit various levels of distribution shift while maintaining semantic
consistency. Therefore, effectively generalizing to samples that have undergone
semantic-preserving transformations, while accurately detecting normal samples
whose semantic meaning has changed after transformation as anomalies, is
crucial for the trustworthiness and reliability of a model. For example,
although it is clear that rotation shifts the meaning for a car in the context
of anomaly detection but preserves the meaning for a bird, current methods are
likely to detect both as abnormal. This complexity underscores the necessity
for dynamic learning procedures rooted in the intrinsic concept of outliers. To
address this issue, we propose new testing protocols and a novel method called
Knowledge Exposure (KE), which integrates external knowledge to comprehend
concept dynamics and differentiate transformations that induce semantic shifts.
This approach enhances generalization by utilizing insights from a pre-trained
CLIP model to evaluate the significance of anomalies for each concept.
Evaluation on CIFAR-10, CIFAR-100, and SVHN with the new protocols demonstrates
superior performance compared to previous methods.
