Litcius/Paper detail

A semantically driven self-supervised algorithm for detecting anomalies in image sets

Bradley J. Wheeler, Hassan A. Karimi

2021Computer Vision and Image Understanding15 citationsDOI

Topics & Concepts

Anomaly detectionComputer scienceArtificial intelligenceSet (abstract data type)Pattern recognition (psychology)Semantic gapImage (mathematics)Anomaly (physics)Supervised learningAlgorithmMachine learningImage retrievalArtificial neural networkCondensed matter physicsProgramming languagePhysicsAnomaly Detection Techniques and ApplicationsDomain Adaptation and Few-Shot LearningArtificial Immune Systems Applications
A semantically driven self-supervised algorithm for detecting anomalies in image sets | Litcius