A Survey of Transfer Learning for Convolutional Neural Networks

dc.contributor.authorRibani R.
dc.contributor.authorMarengoni M.
dc.date.accessioned2024-03-12T23:51:35Z
dc.date.available2024-03-12T23:51:35Z
dc.date.issued2019
dc.description.abstract© 2019 IEEE.Transfer learning is an emerging topic that may drive the success of machine learning in research and industry. The lack of data on specific tasks is one of the main reasons to use it, since collecting and labeling data can be very expensive and can take time, and recent concerns with privacy make difficult to use real data from users. The use of transfer learning helps to fast prototype new machine learning models using pre-trained models from a source task since training on millions of images can take time and requires expensive GPUs. In this survey, we review the concepts and definitions related to transfer learning and we list the different terms used in the literature. We bring the point of view from different authors of prior surveys, adding some more recent findings in order to give a clear vision of directions for future work in this field of research.
dc.description.firstpage47
dc.description.lastpage57
dc.identifier.doi10.1109/SIBGRAPI-T.2019.00010
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35183
dc.relation.ispartofProceedings - 32nd Conference on Graphics, Patterns and Images Tutorials, SIBGRAPI-T 2019
dc.rightsAcesso Restrito
dc.subject.otherlanguageConvolutional Neural Networks
dc.subject.otherlanguageDeep Learning
dc.subject.otherlanguageTransfer Learning
dc.titleA Survey of Transfer Learning for Convolutional Neural Networks
dc.typeArtigo de evento
local.scopus.citations166
local.scopus.eid2-s2.0-85077179526
local.scopus.subjectConvolutional neural network
local.scopus.subjectEmerging topics
local.scopus.subjectMachine learning models
local.scopus.subjectSpecific tasks
local.scopus.subjectTransfer learning
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077179526&origin=inward
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