Generate-to-adapt model has used generative adversarial network to implement model structure and has made a breakthrough in domain adaptation learning. However

some of its network structures lack information interaction

and the ability to use only adversarial learning is not sufficient to completely reduce the inter-domain distance. In this paper

an unsupervised domain adaptation classification model based on generative adversarial network (UDAG) is proposed. This model optimizes inter-domain differences and makes full use of the information between unsupervised confrontation training and supervised classification training to learn the shared features between the source and target domain distribution. The experimental results under four domain adaptation conditions show that the UDAG model learns better shared feature embedding and implements domain adaptive classification

and the classification accuracy is significantly improved.