Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization
Name
2104.05833.pdf
Description
Accepted version
Size
17.01 MB
Format
Adobe PDF
Checksum (MD5)
bcb1dab054f3a42411feed6cc449c3cc
Author(s)
Li, Daiqing
Yang, Junlin
Kreis, Karsten
Torralba, Antonio
Fidler, Sanja
Date Issued
2021
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
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR46437.2021.00820
