Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization

Author(s)
Li, Daiqing; Yang, Junlin; Kreis, Karsten; Torralba, Antonio; Fidler, Sanja
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Date issued
2021
URI
https://hdl.handle.net/1721.1/143992
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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).
Version: Author's final manuscript
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