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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 whiledoi:10.24963/ijcai.2020/436 dblp:conf/ijcai/XuZWOZS20 fatcat:keropa4wmnhklajkyfwnq67424