Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation
This addresses a specific bottleneck in domain adaptation for machine learning applications, offering an incremental improvement over existing adversarial methods.
The paper tackled the problem of deteriorating domain discriminator capability in unsupervised domain adaptation by proposing RADA, which relabels target domain samples as source samples during training, achieving state-of-the-art results on multiple benchmarks.
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.