In Unsupervised Domain Adaptation(UDA), the hard samples cause serious negative transfer to model performance. However, the existing UDA methods don’t fully pay attention to the fine-grained processing of hard samples in target and source domains. To address these problems, the UDA framework with Hard-sample Dividing and Processing Strategy (HDPS) is proposed in this paper. In HDPS, we define sample division criteria in target domain and source domain respectively and divide the samples into easy samples, hard samples with low confidence and hard samples with high confidence. We further design systematic processing strategy of the three types of samples. Specifically, we firstly define geometric metrics based on the class prototype to divide the hard samples and eliminate them in source domain. We further design two weight allocation strategies based on sample classification entropy in target domain. Finally, we build a teacher-student guidance mechanism to learn the discriminative features of hard samples. The HDPS framework can be easily loaded to other methods as a plug-in, can promote the diversity feature learning of samples, and significantly improve the discriminant performance of the model. Extensive experiments on six benchmark datasets verify that HDPS is effective and superior to most existing UDA methods.
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