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Found 3 papers, 3 papers with code
Parameter Exchange for Robust Dynamic Domain Generalization
The parameters of dynamic networks can be decoupled into a static and a dynamic component, which are designed to learn domain-invariant and domain-specific features, respectively.
Dynamic Domain Generalization
Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains.
Ranked #18 on
Domain Generalization
on DomainNet
Semi-Supervised Domain Generalization with Evolving Intermediate Domain
From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG.
