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dc.contributor.authorSugianto, Nehemia
dc.contributor.authorTjondronegoro, Dian
dc.date.accessioned2019-12-19T01:25:53Z
dc.date.available2019-12-19T01:25:53Z
dc.date.issued2019
dc.identifier.isbn9781728131184
dc.identifier.doi10.1109/ritapp.2019.8932731
dc.identifier.urihttp://hdl.handle.net/10072/389873
dc.description.abstractFor robotics and AI applications, automatic facial expression recognition can be used to measure user’s satisfaction on products and services that are provided through the human-computer interactions. Large-scale datasets are essentially required to construct a robust deep learning model, which leads to increased training computation cost and duration. This requirement is of particular issue when the training is supposed to be performed on an ongoing basis in devices with limited computation capacity, such as humanoid robots. Knowledge transfer has become a commonly used technique to adapt existing models and speed-up training process by supporting refinements on the existing parameters and weights for the target task. However, most state-of-the-art facial expression recognition models are still based on a single stage training (train at once), which would not be enough for achieving a satisfactory performance in real world scenarios. This paper proposes a knowledge transfer method to support learning using cross-domain datasets, from generic to specific domain. The experimental results demonstrate that shorter and incremental training using smaller-gap-domain from cross-domain datasets can achieve a comparable performance to training using a single large dataset from the target domain.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencename7th International Conference on Robot Intelligence Technology and Applications (RiTA 2019)
dc.relation.ispartofconferencetitle2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA)
dc.relation.ispartofdatefrom2019-11-01
dc.relation.ispartofdateto2019-11-03
dc.relation.ispartoflocationDaejeon, Korea
dc.relation.ispartofpagefrom205
dc.relation.ispartofpagefrom5 pages
dc.relation.ispartofpageto209
dc.relation.ispartofpageto5 pages
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleCross-Domain Knowledge Transfer for Incremental Deep Learning in Facial Expression Recognition
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationSugianto, N; Tjondronegoro, D, Cross-Domain Knowledge Transfer for Incremental Deep Learning in Facial Expression Recognition, 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA), 2019
dc.date.updated2019-12-19T01:02:01Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 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.
gro.hasfulltextFull Text
gro.griffith.authorTjondronegoro, Dian W.


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