  Semi-supervised domain adaptation (SSDA), which aims to learn models in a
partially labeled target domain with the assistance of the fully labeled source
domain, attracts increasing attention in recent years. To explicitly leverage
the labeled data in both domains, we naturally introduce a conditional GAN
framework to transfer images without changing the semantics in SSDA. However,
we identify a label-domination problem in such an approach. In fact, the
generator tends to overlook the input source image and only memorizes
prototypes of each class, which results in unsatisfactory adaptation
performance. To this end, we propose a simple yet effective Relaxed conditional
GAN (Relaxed cGAN) framework. Specifically, we feed the image without its label
to our generator. In this way, the generator has to infer the semantic
information of input data. We formally prove that its equilibrium is desirable
and empirically validate its practical convergence and effectiveness in image
transfer. Additionally, we propose several techniques to make use of unlabeled
data in the target domain, enhancing the model in SSDA settings. We validate
our method on the well-adopted datasets: Digits, DomainNet, and Office-Home. We
achieve state-of-the-art performance on DomainNet, Office-Home and most digit
benchmarks in low-resource and high-resource settings.
