Abstract: Supervised deep learning can reach good results in the areas with large amounts of labeled data,but the rea-lity is that there are only a lot of unlabeled data in many areas.How to take advantages of large amounts of unlabeled data has become a key issue in the development of deep learning.Domain adaptation is an effective way to solve this problem.At present,domain adaptation methods based on adversarial training have achieved a good effect.This method uses domain classification loss to align the feature distribution of source domain,and target domain and reduce the difference of distribution between the feature representations of two domain,so the classifier trained with source domain data can be applied to target domain data.The existing domain adaptation method trains the model on the features after domain adaptation and does not make full use of the original information of the target domain data.When the differences between two domains are large,the intra-domain discriminability of target domain features will be reduced.In view of the disadvantages of the present methods,this paper proposed a method for clustering target domain data to assist feature alignment(CAFA-DA) based on the adversarial discriminative domain adaptation (ADDA).Pseudo-labels of target domain data are obtained by clustering and the feature encoder training is constrained in the domain adaptation stage,and the original information of the target domain data is used to improve the discriminability of target domain features.Classifiers trained in the two processes of clustering and domain adaptation are used for ensemble learning and high confidence samples are trained to improve the final effect of the model.The CAFA-DA can be applied to any domain adaption method based on adversarial loss.Finally,this paper compared CAFA-DA with several advanced domain adaption methods on four standard domain adaption data sets.The results show that the accuracy of the CAFA-DA method is better than other methods.Compared with the ADDA method,the results of two comparative experiments are improved by 3.2% and 17.2% respectively.

Key words: Clustering, Domain adaption, Ensemble learning, Feature alignment, Feature representation