  Many unsupervised domain adaptation (UDA) methods exploit domain adversarial
training to align the features to reduce domain gap, where a feature extractor
is trained to fool a domain discriminator in order to have aligned feature
distributions. The discrimination capability of the domain classifier w.r.t the
increasingly aligned feature distributions deteriorates as training goes on,
thus cannot effectively further drive the training of feature extractor. In
this work, we propose an efficient optimization strategy named Re-enforceable
Adversarial Domain Adaptation (RADA) which aims to re-energize the domain
discriminator during the training by using dynamic domain labels. Particularly,
we relabel the well aligned target domain samples as source domain samples on
the fly. Such relabeling makes the less separable distributions more separable,
and thus leads to a more powerful domain classifier w.r.t. the new data
distributions, which in turn further drives feature alignment. Extensive
experiments on multiple UDA benchmarks demonstrate the effectiveness and
superiority of our RADA.
