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dc.contributor.authorLi, Daiqing
dc.contributor.authorYang, Junlin
dc.contributor.authorKreis, Karsten
dc.contributor.authorTorralba, Antonio
dc.contributor.authorFidler, Sanja
dc.date.accessioned2022-07-22T17:16:39Z
dc.date.available2022-07-22T17:16:39Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/143992
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/CVPR46437.2021.00820en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleSemantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationen_US
dc.typeArticleen_US
dc.identifier.citationLi, Daiqing, Yang, Junlin, Kreis, Karsten, Torralba, Antonio and Fidler, Sanja. 2021. "Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization." 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journal2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-07-22T17:12:49Z
dspace.orderedauthorsLi, D; Yang, J; Kreis, K; Torralba, A; Fidler, Sen_US
dspace.date.submission2022-07-22T17:13:04Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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