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.

本文提出了一种名为可强化对抗域自适应（RADA）的有效优化策略，通过使用动态域标签使领域鉴别器重新激活，使目标域样本更加可分离并进一步推动特征对齐，在多个无监督领域自适应基准上进行的广泛实验表明了我们的 RADA 的有效性和优越性。