Litcius/Paper detail

Deep Bayesian Self-Training

Fabio De Sousa Ribeiro, Francesco Calivá, Mark Swainson, Kjartan Gudmundsson, Georgios Leontidis, Stefanos Kollias

2020Aberdeen University Research Archive (Aberdeen University)36 citationsDOIOpen Access PDF

Abstract

Acknowledgements The authors would like to thank Mr. George Marandianos, Mrs. Mamatha Thota and Mr. Samuel Bond-Taylor for manually annotating datasets used in this study and of course the reviewers for their constructive feedback that helped to improve the manuscript. We would also like to thank Professor Luc Bidaut for enabling this collaboration. Funding The research presented in this paper was funded by Engineering and Physical Sciences Research Council (Reference Number EP/R005524/1) and Innovate UK (Reference Number 102908), in collaboration with the Olympus Automation Limited Company, for the project Automated Robotic Food Manufacturing System.

Topics & Concepts

Computer scienceMachine learningArtificial intelligenceInferenceAnnotationLatent variableBayesian probabilityAdaptation (eye)Bayesian inferenceArtificial neural networkDeep learningCluster analysisData miningOpticsPhysicsMachine Learning and Data ClassificationDomain Adaptation and Few-Shot LearningAnomaly Detection Techniques and Applications