Semi-supervised domain adaptation (SSDA) presents a critical hurdle in computer vision, especially given the frequent scarcity of labeled data in real-world settings. This scarcity often causes foundation models, trained on extensive datasets, to underperform when applied to new domains. AdaEmbed, our newly proposed methodology for SSDA, offers a promising solution to these challenges. Leveraging the potential of unlabeled data, AdaEmbed facilitates the transfer of knowledge from a labeled source domain to an unlabeled target domain by learning a shared embedding space. By generating accurate and uniform pseudo-labels based on the established embedding space, the model overcomes the limitations of conventional SSDA, thus enhancing performance significantly. Our method's effectiveness is validated through extensive experiments on benchmark datasets such as DomainNet, Office-Home, and VisDA-C, where AdaEmbed consistently outperforms all the baselines, setting a new state of the art for SSDA. With its straightforward implementation and high data efficiency, AdaEmbed stands out as a robust and pragmatic solution for real-world scenarios, where labeled data is scarce. To foster further research and application in this area, we are sharing the codebase of our unified framework for semi-supervised domain adaptation.

翻译：半监督域适应（SSDA）是计算机视觉领域面临的一个关键挑战，尤其是在现实场景中标记数据频繁稀缺的情况下。这种稀缺性常导致在大型数据集上训练的预训练模型在应用于新领域时表现不佳。我们新提出的SSDA方法——AdaEmbed，为解决这些挑战提供了一个有前景的方案。通过利用未标记数据的潜力，AdaEmbed学习一个共享嵌入空间，从而促进知识从标记源域向未标记目标域的迁移。基于所建立的嵌入空间生成准确且均匀的伪标签，该模型克服了传统SSDA的局限性，从而显著提升了性能。我们的方法在DomainNet、Office-Home和VisDA-C等基准数据集上的大量实验验证了其有效性，在这些数据集上，AdaEmbed始终优于所有基线方法，创下了SSDA的最新水平。凭借其简洁的实现和高数据效率，AdaEmbed在标记数据稀缺的现实场景中脱颖而出，成为一种稳健且实用的解决方案。为促进该领域的进一步研究和应用，我们公开了用于半监督域适应的统一框架的代码库。