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Found 3 papers, 1 papers with code
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling
Detecting whether examples belong to a given in-distribution or are Out-Of-Distribution (OOD) requires identifying features specific to the in-distribution.
Out of Distribution (OOD) Detection
Self-Supervised Anomaly Detection
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A General Framework for Unsupervised Anomaly Detection
In this paper we present GenAD, a simple and generic framework for detecting examples that lie out-of-distribution for a given training set.
Out-of-Distribution Detection
Unsupervised Anomaly Detection
Unsupervised Anomaly Detection From Semantic Similarity Scores
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.
