| dc.contributor.author | Ribeiro, Fabio De Sousa | |
| dc.contributor.author | Calivá, Francesco | |
| dc.contributor.author | Swainson, Mark | |
| dc.contributor.author | Gudmundsson, Kjartan | |
| dc.contributor.author | Leontidis, Georgios | |
| dc.contributor.author | Kollias, Stefanos | |
| dc.date.accessioned | 2020-06-10T10:10:03Z | |
| dc.date.available | 2020-06-10T10:10:03Z | |
| dc.date.issued | 2020-05 | |
| dc.identifier.citation | Ribeiro , F D S , Calivá , F , Swainson , M , Gudmundsson , K , Leontidis , G & Kollias , S 2020 , ' Deep Bayesian Self-Training ' , Neural Computing and Applications , vol. 32 , pp. 4275-4291 . https://doi.org/10.1007/s00521-019-04332-4 | en |
| dc.identifier.issn | 0941-0643 | |
| dc.identifier.other | PURE: 158424258 | |
| dc.identifier.other | PURE UUID: d20f9786-5b02-4860-a764-b993cb131ec5 | |
| dc.identifier.other | ORCID: /0000-0001-6671-5568/work/59336811 | |
| dc.identifier.other | Scopus: 85069661389 | |
| dc.identifier.other | WOS: 000527419900011 | |
| dc.identifier.uri | https://hdl.handle.net/2164/14476 | |
| dc.description | 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. | en |
| dc.format.extent | 17 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Neural Computing and Applications | en |
| dc.rights | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creative commons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | en |
| dc.subject | Machine Learning | en |
| dc.subject | Deep Learning | en |
| dc.subject | Deep learning | en |
| dc.subject | Representation learning | en |
| dc.subject | Bayesian CNN | en |
| dc.subject | Variational inference | en |
| dc.subject | Clustering | en |
| dc.subject | Self-training | en |
| dc.subject | Adaptation | en |
| dc.subject | Uncertainty weighting | en |
| dc.subject | QA75 Electronic computers. Computer science | en |
| dc.subject | Software | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Engineering and Physical Sciences Research Council (EPSRC) | en |
| dc.subject | EP/R005524/1 | en |
| dc.subject | Innovate UK | en |
| dc.subject | 102908 | en |
| dc.subject.lcc | QA75 | en |
| dc.title | Deep Bayesian Self-Training | en |
| dc.type | Journal article | en |
| dc.contributor.institution | University of Aberdeen.Computing Science | en |
| dc.contributor.institution | University of Aberdeen.Machine Learning | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Publisher PDF | en |
| dc.identifier.doi | https://doi.org/10.1007/s00521-019-04332-4 | |
| dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85069661389&partnerID=8YFLogxK | en |
| dc.identifier.vol | 32 | en |
