Unsupervised Domain Adaptation with Duplex Generative Adversarial Network

Unsupervised domain adaptation aims to train a good model for a target domain via transferring knowledge from a related labeled source domain, thus reducing the dependency on huge labeling of target domain samples. Generative adversarial net (GAN) is a newly proposed technique which has shown its capability of alleviating distribution discrepancy. Inspired by GAN, in this work, we propose a novel duplex GAN (DupGAN) which extracts domain invariant and discriminative representation guided by bidirectional domain transformation, formulated as a GAN with duplex discriminators. In addition, each of the duplex discriminators not only judges reality/falsity, but also performs category classification for real images to preserve the category information during domain transformation. As evaluated on the standard benchmarks, i.e., digits datasets and Office-31, our proposed DupGAN outperforms the state-of-the-art methods, indicating its effectiveness on unsupervised domain adaptation.