Abstract
This paper focuses on unsupervised domain adaptation problem, which aims to learn a classification model on an unlabelled target domain by referring to a fully-labelled source domain. Our goal is twofold: bridging the gap between source-target domains, and deriving a discriminative model for the target domain. We propose a Generative Adversarial Guided Learning (GAGL) model to tackle the task. To minimize the source-target domain shift, we adopt the idea of domain adversarial training to build a classification network. Next, to derive a target discriminative classifier, we propose to include a generative network to guide the classifier so as to push its decision boundaries away from high density area of target domain. The proposed GAGL model is an end-to-end framework and thus can simultaneously learn the classification model and refine its decision boundary under the guidance of the generator. Our experimental results show that the proposed GAGL model not only outperforms the baseline domain adversarial model but also achieves competitive results with state-of-the-art methods on standard benchmarks.