Discriminative Feature Mining and Alignment for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Existing UDA methods mainly learn domain-invariant features by directly aligning the marginal distribution of source and target domains. However, they ignore mining the discriminative information of target data and aligning the cross-domain discriminative features, which may lead to performance degradation. To tackle these two issues simultaneously, we propose a Discriminative Feature Mining and Alignment (DFMA) algorithm for UDA. Specifically, DFMA advances a three-stage Instance-Class-Pseudo (ICP) strategy consisting of instance-level contrastive learning, class-level contrastive learning and pseudo-labeling methods to mine the discriminative structure of target data. Then we conduct the cross-domain discriminative feature alignment by integrating the adversarial-based method which designs a novel conditional domain discriminator, and the discrepancy-based method which leverages the first-order and second-order statistical information of features. Furthermore, we build a reconstruction network to enhance the class-level feature alignment. Extensive experiments on several standard UDA benchmark datasets validate the superiority of our proposed DFMA.