  In this paper, we propose Factorized Adversarial Networks (FAN) to solve
unsupervised domain adaptation problems for image classification tasks. Our
networks map the data distribution into a latent feature space, which is
factorized into a domain-specific subspace that contains domain-specific
characteristics and a task-specific subspace that retains category information,
for both source and target domains, respectively. Unsupervised domain
adaptation is achieved by adversarial training to minimize the discrepancy
between the distributions of two task-specific subspaces from source and target
domains. We demonstrate that the proposed approach outperforms state-of-the-art
methods on multiple benchmark datasets used in the literature for unsupervised
domain adaptation. Furthermore, we collect two real-world tagging datasets that
are much larger than existing benchmark datasets, and get significant
improvement upon baselines, proving the practical value of our approach.
