  Existing adversarial domain adaptation methods mainly consider the marginal
distribution and these methods may lead to either under transfer or negative
transfer. To address this problem, we present a self-adaptive re-weighted
adversarial domain adaptation approach, which tries to enhance domain alignment
from the perspective of conditional distribution. In order to promote positive
transfer and combat negative transfer, we reduce the weight of the adversarial
loss for aligned features while increasing the adversarial force for those
poorly aligned measured by the conditional entropy. Additionally, triplet loss
leveraging source samples and pseudo-labeled target samples is employed on the
confusing domain. Such metric loss ensures the distance of the intra-class
sample pairs closer than the inter-class pairs to achieve the class-level
alignment. In this way, the high accurate pseudolabeled target samples and
semantic alignment can be captured simultaneously in the co-training process.
Our method achieved low joint error of the ideal source and target hypothesis.
The expected target error can then be upper bounded following Ben-David's
theorem. Empirical evidence demonstrates that the proposed model outperforms
state of the arts on standard domain adaptation datasets.
