DC Field | Value | Language |
---|---|---|
dc.contributor.author | MOON, YE BIN | - |
dc.contributor.author | Oh, Tae-Hyun | - |
dc.date.accessioned | 2024-05-07T05:21:48Z | - |
dc.date.available | 2024-05-07T05:21:48Z | - |
dc.date.created | 2024-03-04 | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 2287-5255 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/123149 | - |
dc.description.abstract | In this work, we review the challenges of data scarcity and label inefficiency in deep learning and survey efforts to overcome these challenges. Many label-efficient learning methods have been proposed, but there is still room to develop more effective methods. We introduce potential yet promising directions to achieve label-efficient learning in terms of data, learning methods, and efficient use of prior knowledge. We also present case studies involving the latest methods. | - |
dc.language | English | - |
dc.publisher | 대한전자공학회 | - |
dc.relation.isPartOf | IEIE Transactions on Smart Processing & Computing | - |
dc.title | Label Efficient Learning Methods for Computer Vision Applications | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEIE Transactions on Smart Processing & Computing | - |
dc.identifier.kciid | ART003072985 | - |
dc.citation.title | IEIE Transactions on Smart Processing & Computing | - |
dc.contributor.affiliatedAuthor | MOON, YE BIN | - |
dc.contributor.affiliatedAuthor | Oh, Tae-Hyun | - |
dc.description.journalClass | 2 | - |
dc.description.journalClass | 2 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | kci | - |
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