Graph-in-Graph Contrastive Learning for Semi-Supervised Adaptation

Semi-supervised domain adaptation (SSDA) aims to adapt the model from the labeled source domain to the target domain including few labeled data. Extracting the general features is important to solve SSDA, which is beneficial to promote the model to adapt to the target domain. To this end, in this paper, we propose a novel framework to enhance the generalization of the model which improves the accuracy in the target domain. Particularly, we construct a new graph-in-graph component to model the internal relationship of the input feature, which is helpful for extracting rich and general features. In addition, for large amounts of unlabeled data in the target domain, we use the contrastive loss to optimize the network and extract general representations. We evaluate our framework on three benchmark datasets including Domain-Net, Office-Home, and Office. The extensive experimental results demonstrate the proposed method achieves state-of-the-art performance.