Unsupervised Anomaly Detection From Semantic Similarity Scores
This work addresses the challenging problem of anomaly detection for machine learning models, offering improved generalization capabilities for various data types.
This paper introduces SemSAD, a framework for unsupervised anomaly detection that identifies out-of-distribution (OOD) samples by comparing their semantic similarity to in-distribution training examples. The method achieves near-perfect AUROC values for detecting CIFAR-10 as OOD when CIFAR-100 is the in-distribution, significantly outperforming prior methods.
Classifying samples as in-distribution or out-of-distribution (OOD) is a challenging problem of anomaly detection and a strong test of the generalisation power for models of the in-distribution. In this paper, we present a simple and generic framework, {\it SemSAD}, that makes use of a semantic similarity score to carry out anomaly detection. The idea is to first find for any test example the semantically closest examples in the training set, where the semantic relation between examples is quantified by the cosine similarity between feature vectors that leave semantics unchanged under transformations, such as geometric transformations (images), time shifts (audio signals), and synonymous word substitutions (text). A trained discriminator is then used to classify a test example as OOD if the semantic similarity to its nearest neighbours is significantly lower than the corresponding similarity for test examples from the in-distribution. We are able to outperform previous approaches for anomaly, novelty, or out-of-distribution detection in the visual domain by a large margin. In particular, we obtain AUROC values close to one for the challenging task of detecting examples from CIFAR-10 as out-of-distribution given CIFAR-100 as in-distribution, without making use of label information.