  Unsupervised domain adaptation (UDA) aims to improve the classification
performance on an unlabeled target domain by leveraging information from a
fully labeled source domain. Recent approaches explore domain-invariant and
class-discriminant representations to tackle this task. These methods, however,
ignore the interaction between domain alignment learning and class
discrimination learning. As a result, the missing or inadequate tradeoff
between domain alignment and class discrimination are prone to the problem of
negative transfer. In this paper, we propose Dynamic Weighted Learning (DWL) to
avoid the discriminability vanishing problem caused by excessive alignment
learning and domain misalignment problem caused by excessive discriminant
learning. Technically, DWL dynamically weights the learning losses of alignment
and discriminability by introducing the degree of alignment and
discriminability. Besides, the problem of sample imbalance across domains is
first considered in our work, and we solve the problem by weighing the samples
to guarantee information balance across domains. Extensive experiments
demonstrate that DWL has an excellent performance in several benchmark
datasets.
