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dc.contributor.authorSkaldebø, Martin Breivik
dc.contributor.authorSans-Muntadas, Albert
dc.contributor.authorSchjølberg, Ingrid
dc.date.accessioned2019-12-05T07:04:06Z
dc.date.available2019-12-05T07:04:06Z
dc.date.created2019-12-04T15:10:18Z
dc.date.issued2019
dc.identifier.citationOceans. 2019, 17-20. June, Marseille.nb_NO
dc.identifier.issn0197-7385
dc.identifier.urihttp://hdl.handle.net/11250/2631824
dc.description.abstractThis paper investigates a method for reducing the reality gap that occurs when applying simulated data in training for vision-based operations in a subsea environment. The distinction in knowledge in the simulated and real domains is denoted the reality gap. The objective of the presented work is to adapt and test a method for transferring knowledge obtained in a simulated environment into the real environment. The main method in focus is the machine learning framework CycleGAN, mapping desired features in order to recreate environments. The overall goal is to enable a framework trained in a simulated environment to recognize the desired features when applied in the real world. The performance of the learning transfer is measured by the ability to recreate the different environments from new test data. The obtained results demonstrates that the CycleGAN framework is able to map features characteristic for an underwater environment presented with the unlabeled datasets. Evaluation metrics, such as Average precision (AP) or FCN-score can be used to further evaluate the results. Moreover, this requires labeled data, which provides additional development of the current datasets.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleTransfer Learning in Underwater Operationsnb_NO
dc.typeJournal articlenb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.journalOCEANSnb_NO
dc.identifier.doi10.1109/OCEANSE.2019.8867288
dc.identifier.cristin1756723
dc.description.localcode© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,64,20,0
cristin.unitcode194,63,1,0
cristin.unitnameInstitutt for marin teknikk
cristin.unitnameIE fakultetsadministrasjon
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode0


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