Distributed fine-tuning of CNNs for image retrieval on multiple mobile devices

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dc.contributor.authorJang, Gwangseonko
dc.contributor.authorLee, Jin-wooko
dc.contributor.authorLee, Jae-Gilko
dc.contributor.authorLiu, Yunxinko
dc.date.accessioned2020-05-26T03:20:05Z-
dc.date.available2020-05-26T03:20:05Z-
dc.date.created2020-05-25-
dc.date.created2020-05-25-
dc.date.created2020-05-25-
dc.date.created2020-05-25-
dc.date.created2020-05-25-
dc.date.created2020-05-25-
dc.date.issued2020-04-
dc.identifier.citationPERVASIVE AND MOBILE COMPUTING, v.64-
dc.identifier.issn1574-1192-
dc.identifier.urihttp://hdl.handle.net/10203/274293-
dc.description.abstractThe high performance of mobile devices has enabled deep learning to be extended to also exploit its strengths on such devices. However, because their computing power is not yet sufficient to perform on-device training, a pre-trained model is usually downloaded to mobile devices, and only inference is performed on them. This situation leads to the problem that accuracy may be degraded if the characteristics of the data for training and those for inference are sufficiently different. In general, fine-tuning allows a pre-trained model to adapt to a given data set, but it has also been perceived as difficult on mobile devices. In this paper, we introduce our on-going effort to improve the quality of mobile deep learning by enabling fine-tuning on mobile devices. In order to reduce its cost to a level that can be operated on mobile devices, a light-weight fine-tuning method is proposed, and its cost is further reduced by using distributing computing on mobile devices. The proposed technique has been applied to LetsPic-DL, a group photoware application under development in our research group. It required only 24 seconds to fine-tune a pre-trained MobileNet with 100 photos on five Galaxy S8 units, resulting in an excellent image retrieval accuracy reflected a 27-35% improvement.-
dc.languageEnglish-
dc.publisherELSEVIER-
dc.titleDistributed fine-tuning of CNNs for image retrieval on multiple mobile devices-
dc.typeArticle-
dc.identifier.wosid000531562400001-
dc.identifier.scopusid2-s2.0-85081155914-
dc.type.rimsART-
dc.citation.volume64-
dc.citation.publicationnamePERVASIVE AND MOBILE COMPUTING-
dc.identifier.doi10.1016/j.pmcj.2020.101134-
dc.contributor.localauthorLee, Jae-Gil-
dc.contributor.nonIdAuthorJang, Gwangseon-
dc.contributor.nonIdAuthorLee, Jin-woo-
dc.contributor.nonIdAuthorLiu, Yunxin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorMobile deep learning-
dc.subject.keywordAuthorFine tuning-
dc.subject.keywordAuthorCollaborative photography-
dc.subject.keywordAuthorImage retrieval-
dc.subject.keywordAuthorAd-hoc cloud computing-
dc.subject.keywordPlusCLOUD-
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