Recursive Chaining of Reversible Image-to-Image Translators for Face Aging

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Heljakka, Ari
dc.contributor.author Solin, Arno
dc.contributor.author Kannala, Juho
dc.date.accessioned 2019-01-14T09:25:10Z
dc.date.available 2019-01-14T09:25:10Z
dc.date.issued 2018-01-01
dc.identifier.citation Heljakka , A , Solin , A & Kannala , J 2018 , Recursive Chaining of Reversible Image-to-Image Translators for Face Aging . in Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11182 LNCS , SPRINGER , pp. 309-320 , International Conference on Advanced Concepts for Intelligent Vision Systems , Poitiers , France , 24/09/2018 . https://doi.org/10.1007/978-3-030-01449-0_26 en
dc.identifier.isbn 9783030014483
dc.identifier.issn 0302-9743
dc.identifier.issn 1611-3349
dc.identifier.other PURE UUID: e56fb989-7b55-4c4f-995c-995457321bb4
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/recursive-chaining-of-reversible-imagetoimage-translators-for-face-aging(e56fb989-7b55-4c4f-995c-995457321bb4).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85054807571&partnerID=8YFLogxK
dc.identifier.other PURE LINK: https://arxiv.org/abs/1802.05023
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/42971641/1802.05023.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/36037
dc.description.abstract This paper addresses the modeling and simulation of progressive changes over time, such as human face aging. By treating the age phases as a sequence of image domains, we construct a chain of transformers that map images from one age domain to the next. Leveraging recent adversarial image translation methods, our approach requires no training samples of the same individual at different ages. Here, the model must be flexible enough to translate a child face to a young adult, and all the way through the adulthood to old age. We find that some transformers in the chain can be recursively applied on their own output to cover multiple phases, compressing the chain. The structure of the chain also unearths information about the underlying physical process. We demonstrate the performance of our method with precise and intuitive metrics, and visually match with the face aging state-of-the-art. en
dc.format.extent 12
dc.format.extent 309-320
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Springer
dc.relation.ispartof International Conference on Advanced Concepts for Intelligent Vision Systems en
dc.relation.ispartofseries Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings en
dc.relation.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) en
dc.relation.ispartofseries Volume 11182 LNCS en
dc.rights openAccess en
dc.subject.other Theoretical Computer Science en
dc.subject.other Computer Science(all) en
dc.subject.other 113 Computer and information sciences en
dc.title Recursive Chaining of Reversible Image-to-Image Translators for Face Aging en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Professorship Solin A.
dc.subject.keyword Deep learning
dc.subject.keyword Face aging
dc.subject.keyword Face synthesis
dc.subject.keyword GAN
dc.subject.keyword Transfer learning
dc.subject.keyword Theoretical Computer Science
dc.subject.keyword Computer Science(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201901141220
dc.identifier.doi 10.1007/978-3-030-01449-0_26
dc.type.version acceptedVersion


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