  Unsupervised domain adaptation targets to transfer task-related knowledge
from labeled source domain to unlabeled target domain. Although tremendous
efforts have been made to minimize domain divergence, most existing methods
only partially manage by aligning feature representations from diverse domains.
Beyond the discrepancy in data distribution, the gap between source and target
label distribution, recognized as label distribution drift, is another crucial
factor raising domain divergence, and has been under insufficient exploration.
From this perspective, we first reveal how label distribution drift brings
negative influence. Next, we propose Label distribution Matching Domain
Adversarial Network (LMDAN) to handle data distribution shift and label
distribution drift jointly. In LMDAN, label distribution drift is addressed by
a source sample weighting strategy, which selects samples that contribute to
positive adaptation and avoid adverse effects brought by the mismatched
samples. Experiments show that LMDAN delivers superior performance under
considerable label distribution drift.
