A Collaborative Alignment Framework of Transferable Knowledge Extraction for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a label-rich source domain to understand a similar yet distinct unlabeled target domain. Notably, global distribution statistics across domains and local semantic characteristics across samples, are two essential factors of data analysis that should be fully explored. Most existing UDA approaches either harness only one of them or fail to closely associate them for efficient adaptation. In this work, we propose a unified framework, called Collaborative Alignment Framework (CAF), which simultaneously reduces the global domain discrepancy and preserves the local semantic consistency for cross-domain knowledge transfer in a collaborative manner. Specifically, for domain-oriented alignment, we utilize adversarial training or minimize the Wasserstein distance between the two distributions to learn domain-level invariant representations. For semantic-oriented matching, we capture the semantic discrepancy between the predictions of two diverse task-specific classifiers and enhance the features of target data to be near the support of the source data class-wisely, which promotes semantic consistency across domains effectively. These two adaptation processes can be deeply intertwined in CAF via collaborative training, thus CAF can learn domain-invariant and semantic-consistent feature representations. Extensive experiments on four popular benchmarks, including DomainNet, VisDA-2017, Office-31, and ImageCLEF, demonstrate the proposed methods significantly outperform the existing methods, especially on the large-scale dataset. The code is available at https://github.com/BIT-DA/CAF.

Automated summary

This paper proposes a unified framework called Collaborative Alignment Framework (CAF), which reduces global domain discrepancy and preserves local semantic consistency for cross-domain knowledge transfer. It utilizes adversarial training or Wasserstein distance to learn domain-level invariant representations and enhances the features of target data to be consistent with the source data class-wise. Experimental results demonstrate the superiority of the proposed methods over existing methods on popular benchmarks.