22 citations · 57 across the 7 of their papers we have counts for
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cs.LG2019★ 7 cited
Class-imbalanced Domain Adaptation: An Empirical Odyssey
Shuhan Tan, Xingchao Peng, Kate Saenko
Unsupervised domain adaptation is a promising way to generalize deep models to novel domains. However, the current literature assumes that the label distribution is domain-invarian…
cs.LG2019★ 5 cited
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
Shuhan Tan, Jiening Jiao, Wei-Shi Zheng
In conventional domain adaptation, a critical assumption is that there exists a fully labeled domain (source) that contains the same label space as another unlabeled or scarcely la…