Promoting Semantic Connectivity: Dual Nearest Neighbors Contrastive Learning for Unsupervised Domain Generalization

Yuchen Liu, Yaoming Wang, Yabo Chen, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong

Abstract: Domain Generalization (DG) has achieved great success in generalizing knowledge from source domains to unseen target domains. However, current DG methods rely heavily on labeled source data, which are usually costly and unavailable. Thus, we study a more practical unsupervised domain generalization (UDG) problem. Learning invariant visual representation from different views, i.e., contrastive learning, promises well semantic features for in-domain unsupervised learning. However, it fails in cross-domain scenarios. In this paper, we first delve into the failure of vanilla contrastive learning and point out that semantic connectivity is the key to UDG. Specifically, suppressing the intra-domain connectivity and encouraging the intra-class connectivity help to learn the domain-invariant semantic information. Then, we propose a novel unsupervised domain generalization approach, namely Dual Nearest Neighbors contrastive learning with strong Augmentation (DN$^2$A). DN$^2$A leverages strong augmentations to suppress the intra-domain connectivity and proposes a novel dual nearest neighbors search strategy to find trustworthy cross domain neighbors along with in-domain neighbors to encourage intra-class connectivity. Experimental results demonstrate that our DN$^2$A outperforms the state-of-the-art by a large margin, e.g., 12.01% and 13.11% accuracy gain with only 1% labels for linear evaluation on PACS and DomainNet, respectively.

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