dc.contributor.author | Li, Daiqing | |
dc.contributor.author | Yang, Junlin | |
dc.contributor.author | Kreis, Karsten | |
dc.contributor.author | Torralba, Antonio | |
dc.contributor.author | Fidler, Sanja | |
dc.date.accessioned | 2022-07-22T17:16:39Z | |
dc.date.available | 2022-07-22T17:16:39Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/143992 | |
dc.language.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.isversionof | 10.1109/CVPR46437.2021.00820 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | arXiv | en_US |
dc.title | Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Li, 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.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.relation.journal | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2022-07-22T17:12:49Z | |
dspace.orderedauthors | Li, D; Yang, J; Kreis, K; Torralba, A; Fidler, S | en_US |
dspace.date.submission | 2022-07-22T17:13:04Z | |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |