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Found 4 papers, 2 papers with code
Combining inherent knowledge of vision-language models with unsupervised domain adaptation through self-knowledge distillation
In a first step, we generate the zero-shot predictions of the source and target dataset using the vision-language model.
Ranked #1 on
Domain Adaptation
on Office-Home
Gradual Source Domain Expansion for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) tries to overcome the need for a large labeled dataset by transferring knowledge from a source dataset, with lots of labeled data, to a target dataset, that has no labeled data.
Ranked #5 on
Domain Adaptation
on Office-31
Unsupervised Domain Adaptation via Minimized Joint Error
In this paper, we argue that the joint error is essential for the domain adaptation problem, in particular if the samples from different classes in source/target are closely aligned when matching the marginal distributions.
A General Upper Bound for Unsupervised Domain Adaptation
In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation.
