Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation

Mohammad Akhavan Anvari, Rojina Kashefi, Vahid Reza Khazaie, Mohammad Khalooei, Mohammad Sabokrou

Abstract:

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, partic
ularly 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, validating the effectiveness of our
approach.