AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space
Ali Mottaghi Mohammad Abdullah Jamal Serena Yeung Omid Mohareri
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.