InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation

Abstract

Unsupervised domain adaptation aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Recently, self-supervised learning (e.g. contrastive learning) has been extended to cross-domain scenarios for reducing domain discrepancy in either instance-to-instance or instance-to-prototype manner. Although achieving remarkable progress, when the domain discrepancy is large, these methods would not perform well as a large shift leads to incorrect initial pseudo labels. To mitigate the performance degradation caused by large domain shifts, we propose to construct multiple intermediate prototypes for each class and perform cross-domain instance-to-prototype based contrastive learning with these constructed intermediate prototypes. Compared with direct cross-domain self-supervised learning, the intermediate prototypes could contain more accurate label information and achieve better performance. Besides, to learn discriminative features and perform domain-level distribution alignment, we perform intra-domain contrastive learning and domain adversarial training. Thus, the model could learn both discriminative and invariant features. Extensive experiments are conducted on three public benchmarks (ImageCLEF, Office-31, and Office-Home), and the results show that the proposed method outperforms baseline methods.