Diversified Dual Domain-Adversarial Neural Networks

The application cost machine learning methods often rely on the availability of large-scale data collection and annotation, especially in the cases of cross-domain learning. One way to circumvent this cost is constructing models to synthesize data and provide automatic annotation. Although these models are attractive, they often can not be generalized from synthetic images to real-world images. Therefore, domain adaptive algorithm is needed to improve these models, so that they can be applied successfully. In this paper, we propose a novel unsupervised domain adaptive framework codenamed D-DANN inspired by the theory of adversarial learning. We apply the discriminator to diverse the features extracted from dual branch CNN. We can obtain more sufficient shared representation across domains by the proposed dual feature extractors. The framework can be easily adapt to most popular CNN models to improve the representation power. We implement the D-DANN with several popular CNN models including LeNet, AlexNet and so on. Using these D-DANN enhanced neural networks, we conduct extensive experiments on several pairs of domain adaptive validation datasets. The results show that our approach can efficiently enhance domain adaptive capability of general CNN models for unlabeled data.