  Unsupervised domain adaptation (UDA) is a critical problem for transfer
learning, which aims to transfer the semantic information from labeled source
domain to unlabeled target domain. Recent advancements in UDA models have
demonstrated significant generalization capabilities on the target domain.
However, the generalization boundary of UDA models remains unclear. When the
domain discrepancy is too large, the model can not preserve the distribution
structure, leading to distribution collapse during the alignment. To address
this challenge, we propose an efficient UDA framework named Gradually Vanishing
Gap in Prototypical Network (GVG-PN), which achieves transfer learning from
both global and local perspectives. From the global alignment standpoint, our
model generates a domain-biased intermediate domain that helps preserve the
distribution structures. By entangling cross-domain features, our model
progressively reduces the risk of distribution collapse. However, only relying
on global alignment is insufficient to preserve the distribution structure. To
further enhance the inner relationships of features, we introduce the local
perspective. We utilize the graph convolutional network (GCN) as an intuitive
method to explore the internal relationships between features, ensuring the
preservation of manifold structures and generating domain-biased prototypes.
Additionally, we consider the discriminability of the inner relationships
between features. We propose a pro-contrastive loss to enhance the
discriminability at the prototype level by separating hard negative pairs. By
incorporating both GCN and the pro-contrastive loss, our model fully explores
fine-grained semantic relationships. Experiments on several UDA benchmarks
validated that the proposed GVG-PN can clearly outperform the SOTA models.
