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Towards Fair Cross-Domain Adaptation via Generative Learning
[article]
2020
arXiv
pre-print
Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. Existing DA normally assumes the well-labeled source domain is class-wise balanced, which means the size per source class is relatively similar. However, in real-world applications, labeled samples for some categories in the source domain could be extremely few due to the difficulty of data collection and annotation, which leads to
arXiv:2003.02366v2
fatcat:73q2wegggjhixo462wya2czore