Minimising disparity in distribution for unsupervised domain adaptation by preserving the local spatial arrangement of data
Domain adaptation is used for machine learning tasks, when the distribution of the training (obtained from source domain) set differs from that of the testing (referred as target domain) set. In the work presented in this study, the problem of unsupervised domain adaptation is solved using a novel o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-08-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2015.0322 |